Abstract: A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Abstract: A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Abstract: A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Abstract: A system and method for camera-based heart rate tracking. The method includes: determining bit values from a set of bitplanes in a captured image sequence that represent the HC changes; determining a facial blood flow data signal for each of a plurality of predetermined regions of interest (ROIs) of the subject captured by the images based on the HC changes; applying a band-pass filter of a passband approximating the heart rate to each of the blood flow data signals; applying a Hilbert transform to each of the blood flow data signals; adjusting the blood flow data signals from revolving phase-angles into linear phase segments; determining an instantaneous heart rate for each the blood flow data signals; applying a weighting to each of the instantaneous heart rates; and averaging the weighted instantaneous heart rates.

Abstract: A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Abstract: A system and method for camera-based heart rate tracking. The method includes: determining bit values from a set of bitplanes in a captured image sequence that represent the HC changes; determining a facial blood flow data signal for each of a plurality of predetermined regions of interest (ROIs) of the subject captured by the images based on the HC changes; applying a band-pass filter of a passband approximating the heart rate to each of the blood flow data signals; applying a Hilbert transform to each of the blood flow data signals; adjusting the blood flow data signals from revolving phase-angles into linear phase segments; determining an instantaneous heart rate for each the blood flow data signals; applying a weighting to each of the instantaneous heart rates; and averaging the weighted instantaneous heart rates.

Abstract: A system and method for camera-based heart rate tracking. The method includes: determining bit values from a set of bitplanes in a captured image sequence that represent the HC changes; determining a facial blood flow data signal for each of a plurality of predetermined regions of interest (ROIs) of the subject captured by the images based on the HC changes; applying a band-pass filter of a passband approximating the heart rate to each of the blood flow data signals; applying a Hilbert transform to each of the blood flow data signals; adjusting the blood flow data signals from revolving phase-angles into linear phase segments; determining an instantaneous heart rate for each the blood flow data signals; applying a weighting to each of the instantaneous heart rates; and averaging the weighted instantaneous heart rates.

Abstract: A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Abstract: A system and method for camera-based heart rate tracking. The method includes: determining bit values from a set of bitplanes in a captured image sequence that represent the HC changes; determining a facial blood flow data signal for each of a plurality of predetermined regions of interest (ROIs) of the subject captured by the images based on the HC changes; applying a band-pass filter of a passband approximating the heart rate to each of the blood flow data signals; applying a Hilbert transform to each of the blood flow data signals; adjusting the blood flow data signals from revolving phase-angles into linear phase segments; determining an instantaneous heart rate for each the blood flow data signals; applying a weighting to each of the instantaneous heart rates; and averaging the weighted instantaneous heart rates.

Abstract: A system and method for camera-based heart rate tracking. The method includes: determining bit values from a set of bitplanes in a captured image sequence that represent the HC changes; determining a facial blood flow data signal for each of a plurality of predetermined regions of interest (ROIs) of the subject captured by the images based on the HC changes; applying a band-pass filter of a passband approximating the heart rate to each of the blood flow data signals; applying a Hilbert transform to each of the blood flow data signals; adjusting the blood flow data signals from revolving phase-angles into linear phase segments; determining an instantaneous heart rate for each the blood flow data signals; applying a weighting to each of the instantaneous heart rates; and averaging the weighted instantaneous heart rates.

Abstract: A system and method for camera-based heart rate tracking. The method includes: determining bit values from a set of bitplanes in a captured image sequence that represent the HC changes; determining a facial blood flow data signal for each of a plurality of predetermined regions of interest (ROIs) of the subject captured by the images based on the HC changes; applying a band-pass filter of a passband approximating the heart rate to each of the blood flow data signals; applying a Hilbert transform to each of the blood flow data signals; adjusting the blood flow data signals from revolving phase-angles into linear phase segments; determining an instantaneous heart rate for each the blood flow data signals; applying a weighting to each of the instantaneous heart rates; and averaging the weighted instantaneous heart rates.

Abstract: A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Abstract: The transmission of data is accomplished across a network having wireless and wired interfaces. Data is transferred through a wireless interface from an Access Point to a Client/Bridge and then to one of a Intelligent Electrical Device (IED) connected to the Client/Bridge through a wired connection. Data is transferred from the Access Point to the Client/Bridge through a 4 address mode wireless interface having originator, transmitter, receiver and destination address fields. The Client/Bridge determines context cache information representing the media access control numbers of each of the IEDs connected to the Client/Bridge through the wired interface and transfers this context cache information to the AP. In the event of a failure of the Client/Bridge, the context cache information stored at the AP can be transferred to the Client/Bridge using the wireless interface to facilitate recovery of the Client/Bridge.

Abstract: An open frame chassis has a top opening and a bottom opening permitting ambient airflow. A plurality of modules, each enclosing electrical components in thermal contact with a heat sink area of their corresponding module, can each be inserted in the chassis. Ambient air may flow from the bottom opening across the heat sink area of each module to the top opening to passively cool the modules and electrical components. Key pins guide the modules into place and prevent incorrect insertion of a different type of electrical module not corresponding to the electrical connection of the chassis for that slot. Guide pins on corners of the modules mate with guide holes in the chassis to secure the module to the chassis and decrease vibration. Both sides of the chassis have side openings through which the fins of the modules in the end slots of the chassis may be exposed.

Abstract: An open frame chassis has a top opening and a bottom opening permitting ambient air flow there through. A plurality of modules, each enclosing electrical components which are in thermal contact with a heat sink area of their corresponding module, and each of which can be inserted to an inserted position in the open frame chassis. When the modules are inserted into the open frame chassis, ambient air may flow from the bottom opening of the chassis across the heat sink area of each module to the top opening in order to passively cool the modules and the electrical components enclosed therein. The heat sink area has fins which are separated by a distance of 9 mm to 12 mm and have a height 10 mm to 20 mm. Key pins are associated with the electrical connectors of the chassis to guide the modules into place and prevent incorrect insertion of a different type of electrical module not corresponding to the electrical connection of the chassis for that slot.

Abstract: An open frame chassis has a top opening and a bottom opening permitting ambient air flow there through. A plurality of modules, each enclosing electrical components which are in thermal contact with a heat sink area of their corresponding module, and each of which can be inserted to an inserted position in the open frame chassis. When the modules are inserted into the open frame chassis, ambient air may flow from the bottom opening of the chassis across the heat sink area of each module to the top opening in order to passively cool the modules and the electrical components enclosed therein. The heat sink area has fins which are separated by a distance of 9 mm to 12 mm and have a height 10 mm to 20 mm. Key pins are associated with the electrical connectors of the chassis to guide the modules into place and prevent incorrect insertion of a different type of electrical module not corresponding to the electrical connection of the chassis for that slot.

Abstract: The transmission of data is accomplished across a network having wireless and wired interfaces. Data is transferred through a wireless interface from an Access Point to a Client/Bridge and then to one of a Intelligent Electrical Device (IED) connected to the Client/Bridge through a wired connection. Data is transferred from the Access Point to the Client/Bridge through a 4 address mode wireless interface having originator, transmitter, receiver and destination address fields. The originator address uniquely identifies an IED in the overall network, the transmitter address identifies the AP transmitting the data, the receiver address identifies the Client/Bridge receiving the data through the wireless interface while the destination address refers to one of the IEDs connected to the Client/Bridge through the wired interface. Communication can also be performed in reverse from one of the IEDs connected to the Client/Bridge through the wired interface to an IED in the overall network.