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 system and method for surface inspection of an object using optical coherence tomography (OCT) is provided. The method includes determining a surface profile of the object, the surface profile includes one or more regions on a surface of the object; moving the object relative to the OCT scanner head; and for each of the one or more regions on the surface of the object, performing: determining a working distance where the surface of the object at the respective region is within a present depth of field; determining an angle where the respective region is at the determined working distance from an OCT scanner head; directing the OCT scanner head at the determined angle towards the respective region when the respective region is at the determined working distance along the respective angle; and performing an A-scan of the object when the respective region is within the present depth of field.

Abstract: A system and method for surface inspection of an object using optical coherence tomography (OCT) with anticipatory depth of field adjustment is provided. The method includes determining a present working distance and one or more forward working distances; determining a present depth of field in which the surface of the object is in focus at the location of the present working distance and at as many of the consecutive forward surface locations as determined possible; changing to the present depth of field; performing an A-scan of the object; moving the object such that the scanner head is directed at each of the consecutive forward surface locations determined to be in the present depth of field; and performing an A-scan at each of the consecutive forward surface locations determined to be in the present depth of field.

Abstract: A system and method for acquiring imaging data of an object. The system includes a beam splitter for directing a derivative sample beam towards the object and a derivative reference beam towards a reflective element; a detector for detecting at least one of the returned reference beam and the returned sample beam; a mode switching module for selecting an operating mode between an optical coherence tomography (OCT) mode and a hyperspectral imaging (HSI) mode; and a computing module for receiving the detection from the detector and the mode from the mode switching module, wherein in the OCT mode, the computing device generates OCT imaging data by determining an interference pattern produced by a superposition of the returned reference beam and the returned sample beam, and wherein in the HSI mode, the computing module generating hyperspectral imaging data by determining hyperspectral information of the returned sample beam.

Abstract: A system and method for surface inspection of an object using optical coherence tomography (OCT) is provided. The method includes determining a surface profile of the object, the surface profile includes one or more regions on a surface of the object; moving the object relative to the OCT scanner head; and for each of the one or more regions on the surface of the object, performing: determining a working distance where the surface of the object at the respective region is within a present depth of field; determining an angle where the respective region is at the determined working distance from an OCT scanner head; directing the OCT scanner head at the determined angle towards the respective region when the respective region is at the determined working distance along the respective angle; and performing an A-scan of the object when the respective region is within the present depth of field.

Abstract: A system and method for surface inspection of an object using multiplexed optical coherence tomography (OCT) is provided. The method includes moving the object relative to two or more scanner heads along a direction of travel; alternatingly directing a sample beam to each of the two or more scanner heads; and when the sample beam is directed at each respective scanner head, scanning comprising: steering the sample beam from the respective scanner head to an unscanned region on the surface of the object; and performing an A-scan of the object.

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 system and method for surface inspection of an object using optical coherence tomography (OCT) is provided. The method includes determining a first working distance; determining a first depth of field, based on the first working distance; changing the depth of field to the first depth of field; performing an A-scan of the object; moving the object; determining a subsequent working distance; determining whether the object is in focus at the subsequent working distance, if the object is not in focus: determining a subsequent depth of field based on the subsequent working distance; changing the depth of field to the first depth of field; and performing an A-scan of the object; and otherwise, performing an A-scan of the object.

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 system and method for surface inspection of an object using optical coherence tomography (OCT) with anticipatory depth of field adjustment is provided. The method includes determining a present working distance and one or more forward working distances; determining a present depth of field in which the surface of the object is in focus at the location of the present working distance and at as many of the consecutive forward surface locations as determined possible; changing to the present depth of field; performing an A-scan of the object; moving the object such that the scanner head is directed at each of the consecutive forward surface locations determined to be in the present depth of field; and performing an A-scan at each of the consecutive forward surface locations determined to be in the present depth of field.

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: There is provided systems and methods for laser scanning of one or more surfaces; in a particular embodiment, laser scanning of concrete. The system includes: at least one laser scanning module for acquiring imaging data comprising one or more distance measurements between the laser scanning module and each of the surfaces, according to a laser scanning modality; and a computing module, including one or more processors, in communication with the laser scanning module, for: aggregating the imaging data; processing and denoising the imaging data; and determining the presence or absence of a condition on each of the surfaces based on the imaging data.

Abstract: Systems and methods for surface inspection for imaging an object via an optical coherence tomography (OCT) imaging modality are provided. The system includes an OCT imaging module for generating imaging data from a surface under inspection, including: an electromagnetic radiation source for interrogating the object with light; an optical system having an interferometer for generating an interference pattern corresponding to the light backscattered from the object; and a detector for detecting the interference pattern and generating imaging data therefrom; a motion controller device for moving at least one component of the OCT imaging module relative to the object, the motion controller device moving the OCT imaging module such that a surface of the object is within a depth of field of the OCT imaging module; and a computational module for: aggregating the imaging data; and determining the presence or absence of surface defects in the imaging data.

Abstract: Systems, methods, and modules for detecting a biomarker in a sample are described. A system for detecting presence or absence of a biomarker in a sample includes: a light source for producing electromagnetic radiation for interrogating the sample; a biosensor module including: a waveguide for guiding the electromagnetic radiation, the waveguide exposed to the sample; and a recognition element affixed to the waveguide and configured to bind to the biomarker; a detector for receiving the electromagnetic radiation from the waveguide and detecting a signal corresponding to an interaction of the electromagnetic radiation with the biomarker bound to the recognition element, in accordance with at least one detection modality; and a computing device for analyzing data related to the signal in order to detect presence or absence of the biomarker in the sample.

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 pathogen detection are described. A method for pathogen detection comprises collecting a sample from a subject, combining the sample with amplification substances, performing detection operations on the combined sample, analyzing the detection signals, and disposing of the combined sample. Embodiments of an apparatus for pathogen detection comprise a microfluidic disk having a plurality of reaction chambers for combining a sample and amplification substances. Further described apparatuses for pathogen detection comprise a storage unit, a sensor unit and a disposal unit.

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 account data management and exchange is provided. An embodiment includes a client for initiating a transaction. The client includes an account. The client is operable to create a transaction record. Once the transaction record is created, the apparatus can determine a second client to forward the transaction record to. The second is operable to trigger the performance of the selected action to complete and save said record and update corresponding databases, to reject and delete said record or submit said record for further processing.