Abstract: Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.

Abstract: Systems and methods are provided for determining a set of imaging parameters for an imaging system. A selection of an image is received from a set of images. A modification of certain quality measures is received for the selected image. The modified selected image is mapped to a set of imaging parameters of an imaging system based on the certain quality measures using a trained Deep Reinforcement Learning (DRL) agent.

Abstract: A computer-implemented method of splitting whole building energy consumption includes collecting sensor data from a plurality Variable Air Volume (VAV) boxes in a building, an electric meter in the building, and a gas meter in the building. The total heating energy consumption is split across a plurality of zones within the building using the sensor data, thereby yielding heating energy consumption for each zone. The total cooling energy consumption is split across the plurality of zones using the sensor data, thereby yielding cooling energy consumption for each zone and the total ventilation energy consumption is split across the plurality of zones using the sensor data, thereby yielding ventilation energy consumption for each zone. Zone energy consumption by aggregating the heating energy consumption for the zone, the cooling energy consumption for the zone, and the ventilation energy consumption for the zone.

Abstract: A computer-implemented method of splitting whole building energy consumption includes collecting sensor data from a plurality Variable Air Volume (VAV) boxes in a building, an electric meter in the building, and a gas meter in the building. The total heating energy consumption is split across a plurality of zones within the building using the sensor data, thereby yielding heating energy consumption for each zone. The total cooling energy consumption is split across the plurality of zones using the sensor data, thereby yielding cooling energy consumption for each zone and the total ventilation energy consumption is split across the plurality of zones using the sensor data, thereby yielding ventilation energy consumption for each zone. Zone energy consumption by aggregating the heating energy consumption for the zone, the cooling energy consumption for the zone, and the ventilation energy consumption for the zone.

Abstract: A computer-implemented method of splitting whole building energy consumption includes collecting sensor data from a plurality Variable Air Volume (VAV) boxes in a building, an electric meter in the building, and a gas meter in the building. The total heating energy consumption is split across a plurality of zones within the building using the sensor data, thereby yielding heating energy consumption for each zone. The total cooling energy consumption is split across the plurality of zones using the sensor data, thereby yielding cooling energy consumption for each zone and the total ventilation energy consumption is split across the plurality of zones using the sensor data, thereby yielding ventilation energy consumption for each zone. Zone energy consumption by aggregating the heating energy consumption for the zone, the cooling energy consumption for the zone, and the ventilation energy consumption for the zone.

Abstract: Systems and methods are provided for determining a set of imaging parameters for an imaging system. A selection of an image is received from a set of images. A modification of certain quality measures is received for the selected image. The modified selected image is mapped to a set of imaging parameters of an imaging system based on the certain quality measures using a trained Deep Reinforcement Learning (DRL) agent.

Abstract: A computer-implemented method of splitting whole building energy consumption includes collecting sensor data from a plurality Variable Air Volume (VAV) boxes in a building, an electric meter in the building, and a gas meter in the building. The total heating energy consumption is split across a plurality of zones within the building using the sensor data, thereby yielding heating energy consumption for each zone. The total cooling energy consumption is split across the plurality of zones using the sensor data, thereby yielding cooling energy consumption for each zone and the total ventilation energy consumption is split across the plurality of zones using the sensor data, thereby yielding ventilation energy consumption for each zone. Zone energy consumption by aggregating the heating energy consumption for the zone, the cooling energy consumption for the zone, and the ventilation energy consumption for the zone.

Abstract: Multiple cameras are configured for use in video analytics. A single configuration tool is provided. The interrelationships between cameras are included within the configuration. Using a combination of text entry fields, registration of the cameras on a floor or other map, and marking on images from the cameras, an efficient workflow for configuration may be provided.

Abstract: A system for collaborative energy management and control in a building, including an energy management controller, one or more occupant HMIs that supports two-way communication between building occupants and a facility manager, and between building occupants and the energy management controller, and a facility manager HMI that supports two-way communication between the facility manager and the building occupants, and between the facility manager and the energy management controller, in which the occupant HMI allows building occupants to provide temperature preferences to the facility manager and the energy management controller, and the facility manager HMI allows the facility manager to configure an energy policy for the building as a set of rules and to view occupants' aggregated temperature preferences, and the energy management controller determines an optimum temperature range that resolves conflicting occupant temperature preferences and occupant temperature preferences that conflict with the facility m

Abstract: A system and methods for activating and interacting by a user with at least a 3D object displayed on a 3D computer display by at least the user's gestures which may be combined with a user's gaze at the 3D computer display. In a first instance the 3D object is a 3D CAD object. In a second instance the 3D object is a radial menu. A user's gaze is captured by ahead frame containing at least an endo camera and an exo camera worn by a user. A user's gesture is captured by a camera and is recognized from a plurality of gestures. User's gestures are captured by a sensor and are calibrated to the 3D computer display.

Abstract: Multiple cameras are configured for use in video analytics. A single configuration tool is provided. The interrelationships between cameras are included within the configuration. Using a combination of text entry fields, registration of the cameras on a floor or other map, and marking on images from the cameras, an efficient workflow for configuration may be provided.

Abstract: A method and system for enabling facility managers and occupants of commercial buildings to collaboratively define energy policy for building energy control is disclosed.

Abstract: A specially-configured interactive user interface for use in eye typing takes the form of a three-layer arrangement that allows for controlling computer input with eye gazes. The three-layer arrangement includes an outer, rectangular ring of letters, displayed clockwise in alphabetical order (forming the first layer). A group of “frequently-used words” associated with the letters being typed forms an inner ring (and is defined as the second layer). This second layer of words is constantly updated as the user continues to enter text. The third layer is a central “open” portion of the interface and forms the typing space—the “text box” that will be filled as the user continues to type. A separate row of control/function keys (including mode-switching for upper case vs. lower case, numbers and punctuation) is positioned adjacent to the three-layer on-screen keyboard display.