Abstract: Systems for detecting and localizing a person exhibiting a symptom of infection in a space are provided. The systems include a user interface configured to receive position information of the space and a plurality of connected sensors in the space, wherein the plurality of connected sensors are configured to capture sensor signals from the person exhibiting the symptom of infection. The systems further include a processor configured to input captured sensor signals from the plurality of connected sensors to at least one convolutional neural network (CNN) model selected based on a confidence value, wherein the processor is further configured to locate the symptomatic person. The systems further include a graphical user interface connected to the processor and configured to display the location of the person exhibiting the symptom of infection within the space.

Abstract: A system for evaluating a workspace surface having one or more objects placed thereon is disclosed. The system may include sensors configured to capture data corresponding to the workspace surface and a controller communicatively coupled to the sensors. The controller may generate an illuminance map of the workspace surface based on the captured data. The controller may generate a clutter entropy level based on the illuminance map and an entropy assessor. The controller may determine whether the clutter entropy level exceeds a clutter threshold. The controller may provide a clutter indication to a user device if the clutter entropy level exceeds the clutter threshold. The controller may identify the objects placed on the workspace surface. The controller may generate a decluttered layout based on the one or more objects and a layout generator.

Abstract: A method for optimizing a neural network is provided, including: (1) capturing, via a first sensor group having a first field of view, a first sample set having a first sensor domain corresponding to the first field of view; (2) capturing, via a second sensor group having a second field of view, a second sample set having a second sensor domain corresponding to the second field of view; (3) generating regions of interest of the second sample set; (4) translating the regions of interest to the first sensor domain; (5) identifying nodes of the neural network which correspond to the translated regions; and (6) optimizing the neural network by at least one of (a) increasing the weight value of the nodes corresponding to the one or more translated regions and (b) decreasing the weight value of the nodes not corresponding to the one or more translated regions.

Abstract: A method of predicting a data point from a predictor, wherein the predictor comprises a trained machine which has been trained based on a training dataset comprising at least one labelled data point; wherein the method comprises: assigning a first function to the at least one labelled data point and a second function to the data point; determining a level of similarity based on a comparison of the first function and the second function; determining a similarity information between the at least one labelled data point and the data point; assigning a first weight to a prediction from the trained machine for the data point, and a second weight to the similarity information; determining an adjustment to the first and/or the second weight as a function of the level of similarity; and determining a prediction for the data point, wherein the prediction is based on combining the prediction from the trained machine with the adjusted first weight and the similarity information with the adjusted second weight.

Abstract: A system for generating two-dimensional images is provided. The system may include a controller configured to receive a three-dimensional design image. The controller may be further configured to generate a fake 2D image based on the 3D design image and one or more 3D image parameters, such as object orientation. The controller may be further configured to generate a realistic 2D image based on the fake 2D image and one or more image features. The controller may be further configured to evaluate a realism value of the realistic 2D image based on one or more real 2D images. The controller may be further configured to evaluate a detected orientation of the realistic 2D image. The controller may be further configured to update the realistic image generator based on the realism value or the detected orientation.