Abstract: A system and method to create a static (DC) or near static magnetic field either using permanent magnets or a transmitter loop. The field magnetizes the objects as they pass through the field and response based on the volume of the material and the magnetic susceptibility. By making measurements of the resulting magnetic field (B field or in this case dB/dt), the system can measure how magnetized the object becomes. This property can be used alone and just recover information about magnetic susceptibility, or this information can be combined with other physical properties such as permanent magnetization or other properties such as electrical conductivity.

Abstract: A system and method of automated hardware verification and machine learning training using an automated object mover. The automated object mover consists of a ceiling or floor mounted rail. Two vertical poles are mounted on rails and can travel horizontally along the rails. Mechanical mechanisms are included such that the height and trajectory of the object as it passes through the gateway can be modified. Vertical poles are placed between the vertical columns and can travel through the gateway columns. Comparison of collected data from an automated data mover approach compared to a person walking through the gateway system approach produces data similar to an idealized object response for the object moving through the gateway at a constant speed and in zero environmental noise. Different types of collected data can be blended together to create a comprehensive machine learning dataset for training of robust alerting models for various objects.

Abstract: Aspects relate to systems and methods for the application of computer vision and sensor fusion to assist in the management and operation of a facility. For large facilities, many of the expenses and staffing requirements incurred such as energy, custodial duties, maintenance and security can scale with size rather than usage, and therefore be subject to gross inefficiencies. These challenges may arise from a lack of timely information available with which to make such optimizations and improvements. The approaches disclosed leverage recent advancements in computer vision technology to extract actionable information from raw sensor data collected through-out the facility. This information may be processed and applied in either an autonomous, semi-autonomous, or user driven approach to control and manage a number of processes occurring regularly within a facility, such as lighting, cleaning, and security.

Abstract: Aspects relate to systems and methods for the application of computer vision and sensor fusion to assist in the management and operation of a facility. For large facilities, many of the expenses and staffing requirements incurred such as energy, custodial duties, maintenance and security can scale with size rather than usage, and therefore be subject to gross inefficiencies. These challenges may arise from a lack of timely information available with which to make such optimizations and improvements. The approaches disclosed leverage recent advancements in computer vision technology to extract actionable information from raw sensor data collected through-out the facility. This information may be processed and applied in either an autonomous, semi-autonomous, or user driven approach to control and manage a number of processes occurring regularly within a facility, such as lighting, cleaning, and security.

Abstract: Aspects relate to systems and methods for the application of computer vision and sensor fusion to assist in the management and operation of a facility. For large facilities, many of the expenses and staffing requirements incurred such as energy, custodial duties, maintenance and security can scale with size rather than usage, and therefore be subject to gross inefficiencies. These challenges may arise from a lack of timely information available with which to make such optimizations and improvements. The approaches disclosed leverage recent advancements in computer vision technology to extract actionable information from raw sensor data collected through-out the facility. This information may be processed and applied in either an autonomous, semi-autonomous, or user driven approach to control and manage a number of processes occurring regularly within a facility, such as lighting, cleaning, and security.

Abstract: Aspects relate to systems and methods for improving the operation of computer-implemented neural networks. Some aspects relate to training a neural network using a compressed representation of the inputs either through efficient discretization of the inputs, or choice of compression. This approach allows a multiscale approach where the input discretization is adaptively changed during the learning process, or the loss of the compression is changed during the training. Once a network has been trained, the approach allows for efficient predictions and classifications using compressed inputs. One approach can generate a larger more diverse training dataset based on both simulations from physical models, as well as incorporating domain expertise and other available information. One approach can automatically match the documents to the list, while still allowing a user to input information to update and correct the matching process.

Abstract: Aspects relate to systems and methods for improving the operation of computer-implemented neural networks. Some aspects relate to training a neural network using a compressed representation of the inputs either through efficient discretization of the inputs, or choice of compression. This approach allows a multiscale approach where the input discretization is adaptively changed during the learning process, or the loss of the compression is changed during the training. Once a network has been trained, the approach allows for efficient predictions and classifications using compressed inputs. One approach can generate a larger more diverse training dataset based on both simulations from physical models, as well as incorporating domain expertise and other available information. One approach can automatically match the documents to the list, while still allowing a user to input information to update and correct the matching process.

Abstract: Aspects relate to systems and methods for improving the operation of computer-implemented neural networks. Some aspects relate to training a neural network using a compressed representation of the inputs either through efficient discretization of the inputs, or choice of compression. This approach allows a multiscale approach where the input discretization is adaptively changed during the learning process, or the loss of the compression is changed during the training. Once a network has been trained, the approach allows for efficient predictions and classifications using compressed inputs. One approach can generate a larger more diverse training dataset based on both simulations from physical models, as well as incorporating domain expertise and other available information. One approach can automatically match the documents to the list, while still allowing a user to input information to update and correct the matching process.