The Government of the United States of America, As Represented by the Secretary of Homeland Security

Mark A Fry

Barry Thomas Smith

An access control system and method configured to DNA-based identification to verify user identification. The access control system may be configured to access a database of biometric and biographic data. The database may comprise a biometric and biographic dataset associated with users. Matching logic may determine whether a user attempting to gain access to a secured location or system matches a dataset. The access control system may comprise a microfluidics device configured to perform DNA fingerprinting. The access control system may also comprise a silicone processor configured to execute the matching logic.

DNA access control systems

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.

Systems and methods for detecting a travelling object vortex

Systems and methods of detecting a vortex made by a travelling object is disclosed. 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. The vortex similarity engine may be configured to determine or identify certain properties about the travelling object based on the travelling object 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.

A health monitor for monitoring the health a user. The system may comprise an edge computing device, a computer, and a health monitor. The health monitor may comprise a sensor module connected to one or more sensors. The health monitor may comprise a data storage logic configured to store the sensor data. The computer may comprise an emergency dispatch module configured to contact an emergency dispatch system; and a healthcare contact module configured to contact a healthcare provider. The health monitor may comprise a command module configured to receive third party contact information; a biographic intake module configured to receive biographic information about the user; and a third-party contact module configured to send a message to a third party; said message comprising sensor module data and the biographic information of the user.

System and method for monitoring the health of a user

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.

Articulated robotic arms for robotic baggage inspection and swabbing

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.

System and method for training an artificial intelligence (AI) classifier of scanned items

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.

Robotic resolution of scanning alarms

In an example, a sample collection apparatus to collect sample from a detection subject disposed in a sample collection zone includes a circumferential ring tubing surrounding an interior and configured to be moved between first ring tubing position and second ring tubing position. The circumferential ring tubing includes air nozzles along a circumferential length of the ring tubing to direct targeted air flow within the sample collection zone as the ring tubing is moved between the first ring tubing position and the second ring tubing position, to blow air toward the detection subject in the sample collection zone in the interior and to push a sample of the detection subject via an air flow toward a receptacle. Adjustable actuators are affixed along a periphery of the circumferential ring tubing to adjust the circumferential length of the circumferential ring tubing between a first circumferential length and a second circumferential length.

Sample collection by directed air flow for scent detection

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.

System and method of new understandable training for AI as applied to electromagnetic sensor detection

In an example, a sample collection apparatus to collect sample from a detection subject includes a circumferential ring tubing surrounding an interior and configured to be moved between bottom ring tubing position and top ring tubing position. The circumferential ring tubing includes air nozzles along a circumferential length of the ring tubing to direct air flow toward the interior as the ring tubing is moved from the top ring tubing position to the bottom ring tubing position, to blow air toward the detection subject in a sample collection zone in the interior and to push a sample of the detection subject via an air flow toward a platform on which the detection subject is positioned. The sample includes particles and/or vapor of the detection subject. A receptacle is disposed below the platform to collect the sample carried by the air flow through collection openings of the platform to the receptacle.

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Atlantic City, NJ, United States of America

Mark A Fry