Abstract: Example computer-implemented methods and systems for estimating geophysical fields for magnetic navigation. One example computer-implemented method includes storing, at a navigation object, an offline baseline estimation model. Online geophysical field model data not stored on the navigation object are received at various times at the navigation object. Control logic is used to select at least one of (1) the offline baseline estimation model and (2) the online geophysical field model data to use to estimate geophysical fields for the navigation object at a variety of specified times.

Abstract: This application is directed to a local positioning system having a plurality of wire coils. Each wire coil includes one or more respective turns of wire that have a respective shape and a respective size and are arranged substantially in parallel with a respective wire plane. Each wire coil is configured to be electrically driven by a respective synchronous electric current carrying a respective train of pseudo-random waveforms according to a predefined bandwidth. For each wire coil, the respective train of pseudo-random waveforms includes a first number of waveform periods and is orthogonal to each other train of pseudo-random waveforms of the wire coils. In some embodiments, a receiver system is coupled to, and measures, the magnetic field created by the wire coils during each waveform period. The location of the receiver system is determined based on measured magnetic data vectors of the magnetic field.

Abstract: A method applies secure cryptographic communication between legacy applications and corresponding external services. A user device has one or more processors and memory, and runs a legacy application. The application receives input (e.g., from a user). In response to the input, the application initiates communication over a secure communication protocol to an external service not running on the user device. A client crypto-service encapsulates the communication and provides a secure encrypted tunnel to a server crypto-service, at a server system, in communication with the external service. The server system has one or more processors and memory. The server system receives the encapsulated communication at the server crypto-service and sends the communication to the external service. The server system then receives a response to the communication from the external service and returns the response, through the secure encrypted tunnel, to the user device.

Abstract: Disclosed are exemplary computer-implemented methods and systems for geophysical field sensing based navigation. One example of a computer-implemented method includes: receiving geophysical field data from at least one geophysical field sensor; synchronizing timing of the geophysical field data; de-noising, using a de-noising machine learning model, the geophysical field data removing noise from local sources of noise for the at least one geophysical field sensor to produce de-noised geophysical field data, the de-noising machine learning model trained using ground truth map data and training data corresponding to the ground truth map data; receiving map data from a geophysical map engine; performing error estimation by comparing the de-noised geophysical field data with the map data; and updating a position estimation based at least in part on the error estimation.

Abstract: In an aspect, the present disclosure provides a method comprising: (a) identifying a first negative and positive electromagnetic dipoles in a first electromagnetic field map associated with a heart of the individual at a first time; (b) identifying a second negative and positive electromagnetic dipoles in a second electromagnetic field map associated with the heart of the individual at a second time; (c) determining a first angle based on the first negative and positive electromagnetic dipoles; (d) determining a second angle based on the second negative and positive electromagnetic dipoles; and (e) determining a presence, an absence, or a likelihood of coronary artery disease in the individual, based at least in part on (i) whether the first angle differs from the second angle by at least 100 degrees, or (ii) whether there is a presence of a third electromagnetic dipole in the first or the second electromagnetic field map.

Abstract: Example computer-implemented methods and systems for detecting and removing map artifacts are disclosed. One example computer-implemented method includes obtaining a magnetic map of a region. One or more areas in the magnetic map are determined based on a first machine learning model, where each of the one or more areas includes one or more artifacts in the magnetic map. The one or more areas are determined by generating, based on the first machine learning model and the magnetic map, a second map by the first machine learning model, and comparing the second map with the magnetic map. The one or more artifacts in each of the one or more areas are removed from the magnetic map.

Abstract: An apparatus for measuring magnetic fields from a subject's organ comprises a plurality of unshielded magnetometers in a three-dimensional arrangement. A respective pair of magnetometers, in the plurality of magnetometers, has a respective known separation. Each magnetometer in the plurality of magnetometers is configured to simultaneously detect a biomagnetic field from at least a portion of the subject's organ and a background magnetic field and output a signal indicative of the detected biomagnetic field and the background magnetic field.

Abstract: Example computer-implemented methods and systems for anomaly-sensing based multi-agent navigation are disclosed. One example computer-implemented method includes: receiving relative distance data specifying distance between at least one pair of agents of a plurality of agents, each of a subset of the plurality of agents having an anomaly sensor subsystem; receiving anomaly data from at least one anomaly sensor subsystem of one of the plurality of agents; obtaining pre-surveyed map data; and determining global pose data of the plurality of agents based on the relative distance data and based on comparing the anomaly data to the pre-surveyed map data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery performance prediction. One of the methods includes actions of receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property of at least two battery tests. Each battery test includes applying pulses on the battery cell during a battery cycle. The battery test data is provided as input to a machine learning system to predict battery cell performance. The machine learning system includes a machine learning model that has been trained using training data includes test data of battery cells that reached respective end of life (EOL) cycles. In response, a prediction result for the battery cell is automatically generated by the machine learning model. The prediction result indicates an EOL cycle of the battery cell. An action is taken based on the prediction result.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery defect identification. One of the methods includes receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property in a battery test during at least one portion of a battery cycle. The battery test includes applying one or more pulses on the battery cell. The battery test data of the battery cell is provided as input to a machine learning model running on the computing system to predict whether the battery cell will experience catastrophic fade. The machine learning model has been trained using training data including battery test data of battery cells that experienced catastrophic fade. A prediction result for the battery cell is automatically generated by the machine learning model. An action is taken based on the prediction result for the battery cell.

Abstract: Example computer-implemented methods and systems for anomaly-sensing based multi-agent navigation are disclosed. One example computer-implemented method includes: receiving relative distance data specifying distance between at least one pair of agents of a plurality of agents, each of a first subset of the plurality of agents having an anomaly sensor subsystem; determining a set of relative pose vectors based at least in part on the relative distance data; receiving anomaly data from at least one anomaly sensor subsystem of one of the plurality of agents, obtaining pre-surveyed map data; determining global pose data of the plurality of agents based on the relative distance data and based on comparing the anomaly data to the pre-surveyed map data; and assigning a task to at least one of the plurality of agents based at least in part on a specialized operational capability of the at least one of the plurality of agents.

Abstract: Example computer-implemented methods and systems for estimating geophysical fields for magnetic navigation. One example computer-implemented method includes storing, at a navigation object, an offline baseline estimation model. Online geophysical field model data not stored on the navigation object are received at various times at the navigation object. Control logic is used to select at least one of (1) the offline baseline estimation model and (2) the online geophysical field model data to use to estimate geophysical fields for the navigation object at a variety of specified times.

Abstract: Disclosed are exemplary computer-implemented methods and systems for geophysical field sensing based navigation. One example of a computer-implemented method includes: receiving geophysical field data from at least one geophysical field sensor; synchronizing timing of the geophysical field data; de-noising, using a de-noising machine learning model, the geophysical field data removing noise from local sources of noise for the at least one geophysical field sensor to produce de-noised geophysical field data, the de-noising machine learning model trained using ground truth map data and training data corresponding to the ground truth map data; receiving map data from a geophysical map engine; performing error estimation by comparing the de-noised geophysical field data with the map data; and updating a position estimation based at least in part on the error estimation.