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.
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.
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.
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations using radio waves
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G01C 21/20 - Instruments for performing navigational calculations
G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations
G01S 19/48 - Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
4.
Systems and methods for battery performance prediction
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.
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.
G01C 21/16 - NavigationNavigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigatedDead reckoning by integrating acceleration or speed, i.e. inertial navigation
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
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.
G01C 21/08 - NavigationNavigational instruments not provided for in groups by terrestrial means involving use of the magnetic field of the earth
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
7.
Diagnostic systems and methods for battery defect identification
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.
G01R 31/36 - Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
G01R 31/367 - Software therefor, e.g. for battery testing using modelling or look-up tables
G01R 31/388 - Determining ampere-hour charge capacity or SoC involving voltage measurements
G01R 31/389 - Measuring internal impedance, internal conductance or related variables
G01R 31/392 - Determining battery ageing or deterioration, e.g. state of health
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.
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations using radio waves
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G01C 21/20 - Instruments for performing navigational calculations
G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations
G01S 19/48 - Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
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.
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.
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.
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.
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.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fieldsMeasuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
A61B 5/242 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
A61B 5/243 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetocardiographic [MCG] signals
A61B 5/245 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetoencephalographic [MEG] signals
G01R 33/00 - Arrangements or instruments for measuring magnetic variables
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.
A61B 5/243 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetocardiographic [MCG] signals
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/05 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fieldsMeasuring using microwaves or radio waves
A61B 5/242 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
A61B 5/245 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetoencephalographic [MEG] signals
G01R 33/02 - Measuring direction or magnitude of magnetic fields or magnetic flux
G01R 33/032 - Measuring direction or magnitude of magnetic fields or magnetic flux using magneto-optic devices, e.g. Faraday
G01R 33/04 - Measuring direction or magnitude of magnetic fields or magnetic flux using the flux-gate principle
G01R 33/26 - Arrangements or instruments for measuring magnetic variables involving magnetic resonance for measuring direction or magnitude of magnetic fields or magnetic flux using optical pumping
A61B 90/50 - Supports for surgical instruments, e.g. articulated arms
15.
Signal processing methods and systems for biomagnetic field imaging
A computer system receives a plurality of signals corresponding to first time-series magnetic data generated from a plurality of unshielded magnetometers proximate to the human subject. The first time-series magnetic data corresponds to magnetic fields generated from the human subject. The plurality of signals includes contributions from a biomagnetic field from at least a portion of the subject's organ and a background magnetic field. The computer system synchronizes the first time-series magnetic data to a common clock to generate synchronized time-series magnetic data. The computer system applies one or more filters to the synchronized time-series magnetic data to obtain filtered data. The computer system applies one or more noise reduction techniques to the filtered data to generate updated time-series magnetic data.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/05 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fieldsMeasuring using microwaves or radio waves
A61B 5/243 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetocardiographic [MCG] signals
A61B 5/245 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetoencephalographic [MEG] signals
G01R 33/02 - Measuring direction or magnitude of magnetic fields or magnetic flux
G01R 33/032 - Measuring direction or magnitude of magnetic fields or magnetic flux using magneto-optic devices, e.g. Faraday
G01R 33/04 - Measuring direction or magnitude of magnetic fields or magnetic flux using the flux-gate principle
G01R 33/26 - Arrangements or instruments for measuring magnetic variables involving magnetic resonance for measuring direction or magnitude of magnetic fields or magnetic flux using optical pumping
A61B 90/50 - Supports for surgical instruments, e.g. articulated arms
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.
G01C 21/08 - NavigationNavigational instruments not provided for in groups by terrestrial means involving use of the magnetic field of the earth
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
17.
Navigation via Magnetic Field Localization with Pseudo-random Data Sequences
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.
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.
G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations
patents
