Abstract: Systems and methods of detecting a vortex made by a travelling object is disclosed. Techniques include positioning a media collector to capture a visual media file of the vortex. In some configurations, a graphic recognition algorithm and vortex similarity engine are used to determine whether a visual media file captured by a media collector contains a vortex. In some configurations, a computer may trigger an alert if a travelling object vortex is not expected to be in the visual media file.

Abstract: Systems and methods of detecting a vortex made by a travelling object is disclosed. Techniques include positioning a media collector to capture a visual media file of the vortex. In some configurations, a graphic recognition algorithm and vortex similarity engine are used to determine whether a visual media file captured by a media collector contains a vortex. In some configurations, a computer may trigger an alert if a travelling object vortex is not expected to be in the visual media file.

Abstract: In an example, a meeting system for communication between a client and a host includes a meeting server; a first computer; a secure meeting kiosk including a kiosk interior for a person to enter to attend a first meeting session, the secure meeting kiosk including an access portal for entry into and exit from the kiosk interior; a second computer disposed inside the secure meeting kiosk; a digital lock connected to the access portal to lock the access portal in a locked state and unlock the access portal in an unlocked state; and an external biometric capture device configured to obtain outside biometric information of the person when the person is outside of the kiosk interior. The digital lock switches between the unlocked state and the locked state based at least in part on the outside biometric information obtained by the external biometric capture device.

Abstract: Systems and methods of detecting a vortex made by a travelling object is disclosed. Techniques include positioning a media collector to capture a visual media file of the vortex. In some configurations, a graphic recognition algorithm and vortex similarity engine are used to determine whether a visual media file captured by a media collector contains a vortex. In some configurations, a computer may trigger an alert if a travelling object vortex is not expected to be in the visual media file.

Abstract: Systems and methods of detecting a vortex made by a travelling object is disclosed. Techniques include positioning a media collector to capture a visual media file of the vortex. In some configurations, a graphic recognition algorithm and vortex similarity engine are used to determine whether a visual media file captured by a media collector contains a vortex. In some configurations, a computer may trigger an alert if a travelling object vortex is not expected to be in the visual media file.

Abstract: Systems and methods of detecting a vortex made by a travelling object is disclosed. Techniques include positioning a media collector to capture a visual media file of the vortex. In some configurations, a graphic recognition algorithm and vortex similarity engine are used to determine whether a visual media file captured by a media collector contains a vortex. In some configurations, a computer may trigger an alert if a travelling object vortex is not expected to be in the visual media file.

Abstract: Systems and methods of detecting a vortex made by a travelling object is disclosed. Techniques include positioning a media collector to capture a visual media file of the vortex. In some configurations, a graphic recognition algorithm and vortex similarity engine are used to determine whether a visual media file captured by a media collector contains a vortex. In some configurations, a computer may trigger an alert if a travelling object vortex is not expected to be in the visual media file.

Abstract: Systems and methods for training an artificial intelligence (AI) classifier of scanned items. The items may include a training set of sample raw scans. The set may include in-class objects and not-in-class raw scans. An AI classifier may be configured to sample raw scans in the training set, measure errors in the results, update classifier parameters based on the errors, and detect completion of training.

Abstract: Systems and methods are described, and an example system includes a transport bin configured to carry a baggage item and having spatial reference frame marking detectable by electromagnetic scan and by machine vision. The system includes a robotic arm apparatus at an inspection area, and includes a switched path baggage conveyor that, responsive to electromagnetic scan detection of an object-of-interest (OOI) within the baggage item, conveys the transport bin to the inspection area. The electromagnetic scan generates OOI geometric position information indicating geometric position of the OOI relative to the spatial reference frame marking. The robotic arm apparatus, responsive to receiving the transport bin, uses machine vision to detect orientation of the spatial reference frame marking, then translates OOI geometric position information to local reference frame, for robotic opening of the baggage item, and robotic accessing and contact swab testing on the OOI.

Abstract: Systems and methods for training an artificial intelligence (AI) classifier of scanned items. The items may include a training set of sample raw scans. The set may include in-class objects and not-in-class raw scans. An AI classifier may be configured to sample raw scans in the training set, measure errors in the results, update classifier parameters based on the errors, and detect completion of training.

Abstract: Systems and methods are described, and an example system includes a transport bin configured to carry a baggage item and having spatial reference frame marking detectable by electromagnetic scan and by machine vision. The system includes a robotic arm apparatus at an inspection area, and includes a switched path baggage conveyor that, responsive to electromagnetic scan detection of an object-of-interest (OOI) within the baggage item, conveys the transport bin to the inspection area. The electromagnetic scan generates OOI geometric position information indicating geometric position of the OOI relative to the spatial reference frame marking. The robotic arm apparatus, responsive to receiving the transport bin, uses machine vision to detect orientation of the spatial reference frame marking, then translates OOI geometric position information to local reference frame, for robotic opening of the baggage item, and robotic accessing and contact swab testing on the OOI.

Abstract: Systems and methods are described, and an example method includes a training an artificial intelligence (AI) classifier of scanned items, including obtaining a training set of sample raw scans. The set includes a population of sample in-class raw scans, which include blocks of sensor data from scans of regions having in-class objects, and the set includes a population of sample not-in-class raw scans, which include blocks of sensor data from scan of regions without in-class objects. The example includes applying the AI classifier to sample raw scans in the training set, measuring errors in the results, and updating classifier parameters based on the errors, until detecting a training completion state.

Abstract: Systems and methods are described, and an example system includes a transport bin configured to carry a baggage item and having spatial reference frame marking detectable by electromagnetic scan and by machine vision. The system includes a robotic arm apparatus at an inspection area, and includes a switched path baggage conveyor that, responsive to electromagnetic scan detection of an object-of-interest (OOI) within the baggage item, conveys the transport bin to the inspection area. The electromagnetic scan generates OOI geometric position information indicating geometric position of the OOI relative to the spatial reference frame marking. The robotic arm apparatus, responsive to receiving the transport bin, uses machine vision to detect orientation of the spatial reference frame marking, then translates OOI geometric position information to local reference frame, for robotic opening of the baggage item, and robotic accessing and contact swab testing on the OOI.

Abstract: Systems and methods are described, and an example system includes a transport bin configured to carry a baggage item and having spatial reference frame marking detectable by electromagnetic scan and by machine vision. The system includes a robotic arm apparatus at an inspection area, and includes a switched path baggage conveyor that, responsive to electromagnetic scan detection of an object-of-interest (OOI) within the baggage item, conveys the transport bin to the inspection area. The electromagnetic scan generates OOI geometric position information indicating geometric position of the OOI relative to the spatial reference frame marking. The robotic arm apparatus, responsive to receiving the transport bin, uses machine vision to detect orientation of the spatial reference frame marking, then translates OOI geometric position information to local reference frame, for robotic opening of the baggage item, and robotic accessing and contact swab testing on the OOI.

Abstract: Systems and methods are described, and an example method includes a training an artificial intelligence (AI) classifier of electromagnetic (EM) scanned items, including obtaining a training set of sample raw EM scans. The set includes a population of sample in-class raw EM scans, which include blocks of EM sensor data from EM scans of regions having in-class objects, and the set includes a population of sample not-in-class raw EM scans, which include blocks of EM sensor data from EM scan of regions without in-class objects. The example includes applying the AI classifier to sample raw EM scans in the training set, measuring errors in the results, and updating classifier parameters based on the errors, until detecting a training completion state.

Abstract: Systems and methods are described, and an example system includes a transport bin configured to carry a baggage item and having spatial reference frame marking detectable by electromagnetic scan and by machine vision. The system includes a robotic arm apparatus at an inspection area, and includes a switched path baggage conveyor that, responsive to electromagnetic scan detection of an object-of-interest (OOI) within the baggage item, conveys the transport bin to the inspection area. The electromagnetic scan generates OOI geometric position information indicating geometric position of the OOI relative to the spatial reference frame marking. The robotic arm apparatus, responsive to receiving the transport bin, uses machine vision to detect orientation of the spatial reference frame marking, then translates OOI geometric position information to local reference frame, for robotic opening of the baggage item, and robotic accessing and contact swab testing on the OOI.

Abstract: A method to analyze video includes obtaining an image series in an Eulerian or Lagrangian frame of reference, selecting one or more specific regions of interest and extracting to remove irrelevant motion and/or noise, decomposing the extracted images into a plurality of frequency bands and extracting a pixel value time series corresponding to the values of a pixel in each spatial frequency band, magnifying the pixel value time series to obtain a magnified pixel value time series, adding the magnified pixel value time series to the pixel value time series to generate a superimposed pixel value time series, and applying a spatial reconstruction to the superimposed pixel value time series to generate an output image series. A system to perform this method is also provided.

Abstract: A method to analyze video includes obtaining an image series in an Eulerian or Lagrangian frame of reference, selecting one or more specific regions of interest and extracting to remove irrelevant motion and/or noise, decomposing the extracted images into a plurality of frequency bands and extracting a pixel value time series corresponding to the values of a pixel in each spatial frequency band, magnifying the pixel value time series to obtain a magnified pixel value time series, adding the magnified pixel value time series to the pixel value time series to generate a superimposed pixel value time series, and applying a spatial reconstruction to the superimposed pixel value time series to generate an output image series. A system to perform this method is also provided.

Abstract: A security system to detect motions and/or features associated with a traveler, the security system includes a camera configured to capture an image series of the traveler in an Eulerian or Lagrangian frame of reference, wherein the captured image series contains motions and features associated with the traveler; an alarm system configured to produce an alert signal; and an electronic control unit. The electronic control unit is configured to magnify the motions and features associated with the traveler to generate frame of reference perceptible motions and features, detect characteristic kinematic behaviors of the perceptible motions and features, measure characteristic kinematic quantities of the characteristic kinematic behaviors, determine the presence of features that deviate from a threshold value for the characteristic kinematic quantities, and activate the alarm system to produce the alert signal.

Abstract: A security system to detect motions and/or features associated with a traveler, the security system includes a camera configured to capture an image series of the traveler in an Eulerian or Lagrangian frame of reference, wherein the captured image series contains motions and features associated with the traveler; an alarm system configured to produce an alert signal; and an electronic control unit. The electronic control unit is configured to magnify the motions and features associated with the traveler to generate frame of reference perceptible motions and features, detect characteristic kinematic behaviors of the perceptible motions and features, measure characteristic kinematic quantities of the characteristic kinematic behaviors, determine the presence of features that deviate from a threshold value for the characteristic kinematic quantities, and activate the alarm system to produce the alert signal.