Abstract: A system that includes one or more sensors installed in proximity to a machine configured to collect raw signals associated with an environment of the machine, are multi-layer spatial data that include time-stamp data. The system may include a processor in communication with the sensors and programmed to receive one or more raw signals, denoise the one or more raw signals to obtain a pre-processed signal, extract one or more features from the pre-processed signals, classify the one or more features to an associated class, wherein the associated class includes one or more of a normal class, abnormal class, or a potential-abnormal class, create fusion data by fusing the one or more features utilizing the associated class and the time-stamp data, and output a heat map on an overlaid image of the environment.

Abstract: Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an image source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Abstract: In one embodiment a mobile device includes a long-range transceiver, a medium-range transceiver, and a controller. The controller is configured to receive, from the long-range transceiver, a target ID associated with a remote medium-range transceiver of a remote system, transmit, via the long-range transceiver, an ID, channel, and band of the medium-range transceiver of the remote system, receive packets from the remote medium-range transceiver on the channel, extract received signal strength indicator (RSSI) data from the packets, filter the RSSI data to obtain a maximum RSSI signal within a window of time, in response to the maximum RSSI signal exceeding a threshold, output a signal indictive of the remote system being less than a predetermined distance away.

Abstract: A vehicle side target location method includes receiving a request, via a first wireless connection, for the vehicle to travel to a location, in response to the vehicle being less than a predetermined distance from the location, receiving RF packets, via a second wireless connection having a multiple antenna radio frequency (RF) transceiver having an identification (ID), from a target at the location, identifying packets based on the ID of the RF transceiver, extracting channel state information (CSI) from received signals associated with the identified packets, determining a phase difference of subcarrier phase data of the received signals between each of the multiple antennae, filtering noise of the phase difference of subcarriers based on subcarrier selection to obtain multiple robust phase difference signals, and feeding the multiple robust phase difference signals to a classifier to obtain a side of the vehicle associated with the location of the target.

Abstract: In one embodiment, a mobile device includes a long-range transceiver, a medium-range transceiver, and a controller. The controller is configured to receive, from the long-range transceiver, a medium-range ID associated with a remote medium-range transceiver, transmit, via the long-range transceiver, an ID, channel, and band of the medium-range transceiver, connect, via the medium-range transceiver, to a remote medium-range transceiver based on the medium-range ID, transmit, via the medium-range transceiver, a beacon of data packets at an interval.

Abstract: In one embodiment, a vehicle side target location method includes receiving a request, via a first wireless connection, for the vehicle to travel to a location, in response to the vehicle being less than a predetermined distance from the location, receiving RF packets, via a second wireless connection having a multiple antenna radio frequency (RF) transceiver having an identification (ID), from a target at the location, identifying packets based on the ID of the RF transceiver, extracting channel state information (CSI) from received signals associated with the identified packets, determining an amplitude difference of subcarriers of the received signals between each of the multiple antennae, filtering noise of the amplitude difference of subcarriers based on subcarrier selection to obtain multiple robust amplitude difference signals, and feeding the multiple robust amplitude difference signals to a classifier to obtain a side of the vehicle associated with the location of the target.

Abstract: In one embodiment, a system for a vehicle includes a radio frequency (RF) transceiver having an identification (ID), and a processor coupled with the RF transceiver. The processor is configured to receive a request, via a first wireless connection, for the vehicle to travel to a location, in response to the vehicle being less than a predetermined distance from the location, receive RF packets, via a second wireless connection, from a target at the location, identify packets based on the ID of the RF transceiver, extract received signal strength indicator (RSSI) data from received signals associated with the identified packets, filter the RSSI data to obtain a maximum RSSI signal within a window of time, and in response to the maximum RSSI signal exceeding a threshold, output a signal indictive of the target being less than a predetermined distance from the vehicle.

Abstract: A vehicle system that includes a vehicle seat, wherein the vehicle seat includes a seat-back portion, a seat-bottom portion, a head rest portion, wherein the vehicle seat includes one or more acoustic sensors configured to retrieve an acoustic signal associated with a passenger seat, and a processor in communication with at least the one or more acoustic sensors, wherein the processor is programmed to identify an anomaly associated with the passenger utilizing the acoustic signal, wherein the anomaly is identified via pre-processing the acoustic signal and extracting one or more features associated with the acoustic signal in response to the pre-preprocessing and utilizing a classifier to classify one or more features associated with the acoustic signal as either a normal condition or the anomaly, and output a notification associated with the anomaly in response to identifying the anomaly.

Abstract: Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and/or a radar source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Abstract: A depth-based object-detection convolutional neural network is disclosed. The depth-based object-detection convolutional neural network described herein incorporates a base network and additional structure. The base network is configured to receive a depth image formatted as RGB image data as input, and compute output data indicative of at least one feature of an object in the RGB image data. The additional structure is configured to receive the output data of the base network as input, and compute predictions of the location of a region in the received depth image that includes the object and of a class of the object as output. An object detection device incorporating the depth-based object-detection convolutional neural network is operable in real time using an embedded GPU.

Abstract: A thermal state of a plurality of zones of the building is updated according to a building thermal model and information received from temperature sensors of the building. Predicted occupant counts for an upcoming plurality of time slots for each of the plurality of zones are updated using actual occupancy counts for each of the plurality of zones. A misprediction type distribution for the upcoming plurality of time slots for each of the plurality of zones is updated, the misprediction type distribution indicating misprediction for true negatives, false positives, false negatives, and true positives. A total misprediction cost expectation is updated according to the predicted occupant counts and the misprediction type distribution. HVAC power for each of the plurality of zones is determined to optimize occupant thermal comfort weighted according to the predicted occupant counts while minimizing the total misprediction cost expectation. HVAC operation is controlled per the HVAC power.

Abstract: Occupant thermal comfort may be inferred and improved using body shape information. Height, weight, and shoulder circumference of an occupant of a room may be obtained using a depth sensor. A model may be utilized that is trained on a dataset including information reflecting of occupant comfort within the room versus temperature, the model receiving, as inputs, the height, the weight, and the shoulder circumference of the occupant and environmental information and outputting a comfort class. A temperature set-point for is identified which the room occupant is identified by the model as having the comfort class being indicative of user comfort. Heating, ventilation, and air conditioning (HVAC) controls are adjusted for the room to the identified temperature set-point.

Abstract: A system for image processing includes a first sensor configured to capture at least one or more images, a second sensor configured to capture sound information, a processor in communication with the first sensor and second sensor, wherein the processor is programmed to receive the one or more images and sound information, extract one or more data features associated with the images and sound information utilizing an encoder, output metadata via a decoder to a spatiotemporal reasoning engine, wherein the metadata is derived utilizing the decoder and the one or more data features, determine one or more scenes utilizing the spatiotemporal reasoning engine and the metadata, and output a control command in response to the one or more scenes.

Abstract: Methods and systems for identifying persons utilizing a camera and a wireless transceiver. The wireless transceiver receives wireless packets (e.g., Wi-Fi packets) from one or more mobile devices associated with one or more persons. The channel state information (CSI) is determined based on signals associated with the packets, and first motion characteristics are determined based on the CSI data. Congruently, the camera generates images, and associated image data is received, wherein the image data is associated with the one or more persons. Second motion characteristics are determined based on the image data. The first motion characteristics are matched with the second motion characteristics along with a quantified uncertainty, and the matched motion characteristics are assigned with weights that are based on the second motion characteristics (e.g., image-based). The one or more persons are identified based on the matched motion characteristics and the weights.

Abstract: A system for assisting drivers and riders to find each other in a ride-hailing service is provided. A driver device may communicate with a rider device. The driver device may receive GPS coordinates of the rider device such that the relative location of the rider device can be determined via GPS. In response to the rider device being within a threshold distance from driver device, the rider device and driver device can connect via Wi-Fi to share data to improve the locational ability of the system. At least one processor can receive Wi-Fi data packets from the rider device, measure and extract channel state information (CSI) from the Wi-Fi data packets, execute an angle of arrival (AoA) application to determine the angle of arrival based on the CSI, and display a location of the rider based on the determined angle of arrival from the CSI.

Abstract: An occupancy sensing based system for custodial service management is disclosed that allows schedule custodial services based on actual usage of a site, such as a restroom, instead of following a static schedule and hence can potentially save a significant amount of labor cost. The system is very accurate, privacy aware, and easy to use. In addition to allowing custodial management to optimize their workforce, the system is capable of detecting theft of supplies and performing inventory management. It can be useful in airports, hospitals, stadiums, theaters, academic buildings, convention centers, and many other dynamic places.

Abstract: Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and/or a radar source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Abstract: An apparatus comprising a wireless transceiver configured to communicate packet data with a mobile device associated with one or more persons in a vicinity of the wireless transceiver, and a controller in communication with the wireless transceiver and the camera, the controller configured to receive a plurality of packet data from one or more person, wherein the packet data includes at least amplitude information associated with the wireless channel communicating with the wireless transceiver, identify the one or more persons as a customer or employee in response to the packet data, and determine a motion projection of the one or more persons in response to the packet data and identification indicating the customer.

Abstract: A thermal state of a plurality of zones of the building is updated according to a building thermal model and information received from temperature sensors of the building. Predicted occupant counts for an upcoming plurality of time slots for each of the plurality of zones are updated using actual occupancy counts for each of the plurality of zones. A misprediction type distribution for the upcoming plurality of time slots for each of the plurality of zones is updated, the misprediction type distribution indicating misprediction for true negatives, false positives, false negatives, and true positives. A total misprediction cost expectation is updated according to the predicted occupant counts and the misprediction type distribution. HVAC power for each of the plurality of zones is determined to optimize occupant thermal comfort weighted according to the predicted occupant counts while minimizing the total misprediction cost expectation. HVAC operation is controlled per the HVAC power.

Abstract: A method of operating a selected electronic device of a plurality of electronic devices includes generating image data of at least the selected electronic device with an imaging device, displaying a virtual representation of at least the selected electronic device on a display device based on the generated image data, and receiving a user input with an input device. The method further includes associating the user input with the selected electronic device using a controller, transmitting device data to the selected electronic device with a transceiver, and performing an operation with the selected electronic device based on the transmitted device data.