Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for automated underwater camera system control for aquaculture systems. An underwater camera system includes (i) a line on which the underwater camera system is mounted, the line detachably affixed to a feeder that provides feed for aquatic livestock, (ii) a sensor manager, (iii) one or more sensors that are managed by the sensor manager, (iv) a line navigation controller, and (v) a first actuator for controlling a distance between the feeder and the underwater camera system. The one or more sensors obtain sensor data and the line navigation controller of the underwater camera system determines a distance to position the underwater camera system beneath the feeder to obtain additional sensor data. The line navigation controller transmits a first message to the first actuator to position the underwater camera system at the determined distance beneath the feeder.
G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinations; Position-fixing by co-ordinating two or more distance determinations
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling a remotely operated vehicle (ROV) for performing an underwater task. One apparatus includes a watertight housing; a mounting hardware that attaches the watertight housing to the ROV; one or more sensors in the watertight housing, the one or more sensors configured to generate sensor data that is associated with an underwater task; and one or more processors in the watertight housing, the one or more processors configured to: receive the sensor data from the one or more sensors; generate a navigation plan for the ROV using the sensor data; determine, using the navigation plan, control instructions configured to control the ROV to perform the underwater task; and provide the control instructions to an interface of the ROV configured to communicate with the apparatus.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining drone-based water measurements. One of the methods includes receiving, by a drone management system that controls a drone that is configured to collect water samples, data from a simulator that generates simulation data associated with a body of water; receiving, by the drone management system, an indication that the drone is available to collect one or more water samples from the body of water; determining, by the drone management system and based on the data from the simulator, one or more locations associated with the body of water at which the drone is to collect one or more respective water samples; and; transmitting, by the drone management system, the one or more locations associated with the body of water to the drone.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for microplastic removal. In some implementations, a method can include controlling a camera to capture one or more images of plastic in water; providing the one or more images to a machine learning model trained to detect plastic; obtaining output from the machine learning model indicating one or more items of plastic; and controlling one or more acoustic transducers to move the one or more items of plastic.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a calibration target for ultrasonic removal of ectoparasites from fish. In some implementations, the calibration target includes a fish-shaped structure, sensors positioned at different locations of the fish-shaped structure, a processor that receives sensor values from the sensors, and a transmitter that outputs sensor data from the calibration target based on the sensor values.
Methods, systems, and apparatus, including medium-encoded computer program products, for configuring location-specific electrical load models while preserving privacy. A first machine learning model configured to predict electrical load curves of an electrical utility grid can be obtained from a server. Load values associated with a particular region of the electrical utility grid can be obtained. The load values can be applied as calibration input to the first machine learning model to produce first adjustment parameters for the first machine learning model. The first adjustment parameters can be provided to the server.
H02J 13/00 - Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for developing electrical grid mapping. One of the methods includes obtaining a computer model of an electric power grid; generating a network graph representation of the computer model, wherein nodes of the network graph represent grid assets of the computer model and edges of the network graph represent wires connecting the grid assets; generating an initial prediction of links between nodes in the network graph by adding at least one edge to the network graph to obtain an over-connected graph; applying the over-connected network graph as input to a machine learning model to obtain an annotated network graph, the machine learning model configured to identify edges as positive links and negative links, and apply annotations to the edges indicating whether each edge is a positive or negative link; and updating the model based on the annotated network graph.
H02J 3/00 - Circuit arrangements for ac mains or ac distribution networks
G06F 30/18 - Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Methods, systems, and apparatus, including medium-encoded computer program products, for adjusting an aquaculture camera mounting system. A current combined field of view of two or more cameras that are mounted on an adjustable camera mounting structure in an environment can be determined based upon a current configuration of the adjustable camera mounting structure. A target field of view for the two or more cameras that are mounted on the adjustable camera mounting structure can be determined. Based at least on the field of view target and the current combined field of view, an adjustment parameter for the adjustable camera mounting structure can be determined. The adjustable camera mounting structure can be adjusted according to the adjustment parameter to provide a field of view in accordance with the field of view target.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for developing electrical grid mapping. One of the methods includes obtaining a computer model of an electric power grid; generating a network graph representation of the computer model, wherein nodes of the network graph represent grid assets of the computer model and edges of the network graph represent wires connecting the grid assets; generating an initial prediction of links between nodes in the network graph by adding at least one edge to the network graph to obtain an over-connected graph; applying the over-connected network graph as input to a machine learning model to obtain an annotated network graph, the machine learning model configured to identify edges as positive links and negative links, and apply annotations to the edges indicating whether each edge is a positive or negative link; and updating the model based on the annotated network graph.
G01R 31/08 - Locating faults in cables, transmission lines, or networks
H02J 13/00 - Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
10.
MULTI-SENSOR CALIBRATION OF PORTABLE ULTRASOUND SYSTEM
This disclosure describes a system, method, and non-transitory computer readable media for an ultrasound probe configured to capture ultrasound images of an examination region. The system includes a first set of one or more sensors coupled to the ultrasound probe and configured to estimate a first positional information associated with the ultrasound probe. The system includes a second set of one or more sensors coupled to the ultrasound probe and configured to capture electromagnetic force (EMF) measurements in the examination region to estimate a second positional information associated with the ultrasound probe. The second positional information is used to calibrate the first set of one or more sensors. The system includes a controller configured to use at least one of (i) the first positional information, or (ii) the second positional information to generate a reconstruction of the examination region based on ultrasound images captured by the ultrasound probe.
Methods, systems, and apparatus, including medium-encoded computer program products, for adjusting an aquaculture camera mounting system. A current combined field of view of two or more cameras that are mounted on an adjustable camera mounting structure in an environment can be determined based upon a current configuration of the adjustable camera mounting structure. A target field of view for the two or more cameras that are mounted on the adjustable camera mounting structure can be determined. Based at least on the field of view target and the current combined field of view, an adjustment parameter for the adjustable camera mounting structure can be determined. The adjustable camera mounting structure can be adjusted according to the adjustment parameter to provide a field of view in accordance with the field of view target.
In some embodiments, techniques for optimizing a design for a physical device to be fabricated by a fabrication system is provided. A computing system receives an initial design. The computing system simulates performance of the initial design to determine a simulated performance metric of the initial design. The computing system determines a Jacobian of the simulated performance metric of the initial design. The computing system backpropagates a gradient of the simulated performance metric of the initial design to generate an updated design. The computing system estimates performance of the updated design using the Jacobian of the simulated performance metric of the initial design to determine an estimated performance metric. The computing system backpropagates a gradient of the estimated performance metric to generate a further updated design.
In some embodiments, a computer-implemented method of training and using a machine learning model is provided. A computing system receives a plurality of sampling data values for a geographical area. The computing system creates an interpolated value map and a variance map for the geographical area using the plurality of sampling data values. The computing system trains a machine learning model using values of the interpolated value map as ground truth values and evaluating performance of the machine learning model using the variance map. The computing system stores the trained machine learning model in a model data store.
The present disclosure relates to a method of using a feedback loop from sensors to manipulate sea waves by applying concepts from optics interference and lensing. The method includes capturing, by one or more sensors, environmental data of an aquatic environment, wherein the environmental data relates to one or more of a wind speed, a wind direction, a wave pattern, a wave spectra, and a wave direction. The method includes analyzing, by one or more processors of a controller, the environmental data to identify a sensed environmental condition. Further, the method includes determining an optimal configuration of a wave interference device in the sensed environmental condition, wherein the controller is communicatively coupled to the wave interference device; and configuring the wave interference device to occupy the optimal configuration.
F03B 13/14 - Adaptations of machines or engines for special use; Combinations of machines or engines with driving or driven apparatus; Power stations or aggregates characterised by using wave or tide energy using wave energy
15.
NOISY ECOLOGICAL DATA ENHANCEMENT VIA SPATIOTEMPORAL INTERPOLATION AND VARIANCE MAPPING
In some embodiments, a computer-implemented method of training and using a machine learning model is provided. A computing system receives a plurality of sampling data values for a geographical area. The computing system creates an interpolated value map and a variance map for the geographical area using the plurality of sampling data values. The computing system trains a machine learning model using values of the interpolated value map as ground truth values and evaluating performance of the machine learning model using the variance map. The computing system stores the trained machine learning model in a model data store.
This disclosure describes a system, method, and non-transitory computer readable media for an ultrasound probe configured to capture ultrasound images of an examination region. The system includes a first set of one or more sensors coupled to the ultrasound probe and configured to estimate a first positional information associated with the ultrasound probe. The system includes a second set of one or more sensors coupled to the ultrasound probe and configured to capture electromagnetic force (EMF) measurements in the examination region to estimate a second positional information associated with the ultrasound probe. The second positional information is used to calibrate the first set of one or more sensors. The system includes a controller configured to use at least one of (i) the first positional information, or (ii) the second positional information to generate a reconstruction of the examination region based on ultrasound images captured by the ultrasound probe.
The present disclosure relates to a method of using a feedback loop from sensors to manipulate sea waves by applying concepts from optics interference and lensing. The method includes capturing, by one or more sensors, environmental data of an aquatic environment, wherein the environmental data relates to one or more of a wind speed, a wind direction, a wave pattern, a wave spectra, and a wave direction. The method includes analyzing, by one or more processors of a controller, the environmental data to identify a sensed environmental condition. Further, the method includes determining an optimal configuration of a wave interference device in the sensed environmental condition, wherein the controller is communicatively coupled to the wave interference device; and configuring the wave interference device to occupy the optimal configuration.
Methods, systems, and apparatus for receiving a request for a damage propensity score for a parcel, receiving imaging data for the parcel, wherein the imaging data comprises street-view imaging data of the parcel, extracting, by a machine-learned model including multiple classifiers, characteristics of vulnerability features for the parcel from the imaging data, determining, by the machine-learned model and from the characteristics of the vulnerability features, a damage propensity score for the parcel, and providing a representation of the damage propensity score for display.
09 - Scientific and electric apparatus and instruments
Goods & Services
Electric sensors; sensors for determining location,
acceleration, proximity, motion, position, pressure, light,
temperature, and humidity; tracking sensors; optical
sensors; sensors to record, measure, survey, process, track,
and reproduce data, video, images, sounds, and measurements
and wirelessly transmit the data, video, images, sounds, and
measurements to a computer; GPS tracking devices; electronic
communication devices using radio frequency or Bluetooth
communications for tracking assets, packages, and cargo in
transit; electronic devices for viewing, processing,
monitoring, tracking, managing, and providing information
and predictive recommendations to customers on temperature,
sensor location, inventory, freight and transportation
logistics, location of cargo, packages and assets in
transit, and warehouse and supply chain management;
temperature indicator labels, not for medical purposes; data
processing apparatus.
09 - Scientific and electric apparatus and instruments
Goods & Services
Electric sensors; sensors for determining location,
acceleration, proximity, motion, position, pressure, light,
temperature, and humidity; tracking sensors; optical
sensors; sensors to record, measure, survey, process, track,
and reproduce data, video, images, sounds, and measurements
and wirelessly transmit the data, video, images, sounds, and
measurements to a computer; GPS tracking devices; electronic
communication devices using radio frequency or Bluetooth
communications for tracking assets, packages, and cargo in
transit; electronic devices for viewing, processing,
monitoring, tracking, managing, and providing information
and predictive recommendations to customers on temperature,
sensor location, inventory, freight and transportation
logistics, location of cargo, packages and assets in
transit, and warehouse and supply chain management;
temperature indicator labels, not for medical purposes; data
processing apparatus.
21.
Robotic Gripping Device for Grasping Handles and Objects
An apparatus is described comprising a first gripping component having a first proximal region having a first rigid geometry configured to receive a handle of a tool and a first distal region having a first shape-adaptive finger and a second gripping component having a second proximal region having a second rigid geometry configured to receive the handle of the tool and a second distal region having a second shape-adaptive finger. The first distal region is separated by a clearance from the second distal region when the first rigid geometry and the second rigid geometry are grasping the handle of the tool and, in an absence of the handle of the tool, the first proximal region and the second proximal region enable a pinch grip between the first shape-adaptive finger of the first gripping component and the second shape-adaptive finger of the second gripping component.
The disclosure provides a method for adjusting an optical link alignment of a first communication device with a remote device. The method includes transmitting or receiving an optical signal; receiving one or more measurements of at least one environmental factor at the first communication device or the remote device; and receiving or detecting an apparent amount of alignment of the optical signal. Then, by one or more processors of the first communication device, determining an estimated error attributable to optical cross coupling and an actual amount of alignment of the optical signal based on the apparent amount of alignment and the estimated error. Next, adjusting the first communication device based on the actual amount of alignment to correct for optical cross coupling.
H04B 10/80 - Optical aspects relating to the use of optical transmission for specific applications, not provided for in groups , e.g. optical power feeding or optical transmission through water
H04B 10/112 - Line-of-sight transmission over an extended range
A multi-dimensional latent space (defined by an Encoder model) corresponds to projections of sequences of aptamers. An architecture of tire Encoder model, a hyperparameter of the Encoder model, or a characteristic of a training data set used to train the Encoder model was selected using an assessment of an encoding-efficiency of the Encoder model that is based on: a predicted extents to which representations in an embedding space are indicative of specific aptamer sequences to which a probability distribution of the embedding space differs from a probability distribution of a source space that represents individual base-pairs; generating projections in the latent space using representations of aptamers and the Encoder model; identifying one or more candidate aptamers for the particular target using the projections and the Decoder model; and outputting an identification of the one or more candidate aptamers.
09 - Scientific and electric apparatus and instruments
Goods & Services
Data processing apparatus; RFID readers; RFID readers to
record, measure, survey, process, track, and reproduce data,
video, images, sounds, measurements, location, acceleration,
proximity, motion, position, pressure, temperature, and
humidity, and wirelessly transmit the data, video, images,
sounds, and measurements to a computer; GPS tracking
devices; electronic devices for viewing, processing,
monitoring, tracking, managing, and providing information
and predictive recommendations to customers on temperature,
sensor location, inventory, freight and transportation
logistics, location of cargo, packages and assets in
transit, and warehouse and supply chain management; data
collection devices, namely, optical readers; wireless
communication devices for voice, data, or image
transmission; computer hardware, namely, wireless access
point (WAP) devices that emit, broadcast, or aggregate data
from beacons; computer networking hardware; mobile computing
and operating platforms consisting of data transceivers,
wireless networks and gateways for collection and management
of data; scanners; digital input and output scanners;
electric sensors; sensors for determining location,
acceleration, proximity, motion, position, pressure, light,
temperature, and humidity; tracking sensors; optical
sensors; sensors to record, measure, survey, process, track,
and reproduce data, video, images, sounds, and measurements
and wirelessly transmit the data, video, images, sounds, and
measurements to a computer; GPS tracking devices; electronic
communication devices using radio frequency or bluetooth
communications for tracking assets, packages, and cargo in
transit; temperature indicator labels, not for medical
purposes.
25.
USING FABRICATION MODELS BASED ON LEARNED MORPHOLOGICAL OPERATIONS FOR DESIGN AND FABRICATION OF PHYSICAL DEVICES
In some embodiments, techniques for optimizing a design for a physical device to be fabricated by a fabrication system is provided. A computing system receives an initial design. The computing system uses a fabrication model to determine structural parameters based on the initial design, wherein using the fabrication model includes applying one or more morphological transformations to the initial design that are predicted to be introduced by the fabrication system. The computing system obtains a performance metric by simulating performance of the structural parameters. The computing system determines a loss metric based on the performance metric. The computing system backpropagates a gradient of the loss metric to generate an updated design.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 30/367 - Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
26.
HIERARCHICAL GRAPH CLUSTERING TO ENSEMBLE, DENOISE, AND SAMPLE FROM SELEX DATASETS
Some techniques relate to projecting aptamer representations into an embedding space and clustering the representations. A cluster-specific binding metric can be defined for each cluster based on aptamer-specific binding metrics of aptamers associated with the cluster. A subset of the clusters can be selected based on the cluster-specific binding metrics. Identifications of aptamers assigned to the subset of clusters can then be output.
A multi-dimensional latent space (defined by an Encoder model) corresponds to projections of sequences of aptamers. An architecture of the Encoder model, a hyperparameter of the Encoder model, or a characteristic of a training data set used to train the Encoder model was selected using an assessment of an encoding-efficiency of the Encoder model that is based on: a predicted extents to which representations in an embedding space are indicative of specific aptamer sequences to which a probability distribution of the embedding space differs from a probability distribution of a source space that represents individual base-pairs; generating projections in the latent space using representations of aptamers and the Encoder model; identifying one or more candidate aptamers for the particular target using the projections and the Decoder model; and outputting an identification of the one or more candidate aptamers.
G16B 40/00 - ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
This disclosure describes a system and method for effectively training a machine learning model to identify features in DAS and/or seismic imaging data with limited or no human labels. This is accomplished using a masked autoencoder (MAE) network that is trained in multiple stages. The first stage is a self-supervised learning (SSL) stage where the model is generically trained to predict data that has been removed (masked) from an original dataset. The second stage involves performing additional predictive training on a second dataset that is specific to a particular geographic region, or specific to a certain set of desired features. The model is fine-tuned using labeled data in order to develop feature extraction capabilities.
Aspects of the disclosure provide systems including linear material and tracking tags. The tracking tag may be at least initially arranged on the liner material. The tracking tag may also include a top layer, beacon transmission circuitry, a bottom layer of adhesive, and an activation mechanism. The activation mechanism may be configured to activate the tracking tag and cause the beacon transmission circuitry to transmit beacon signals in order to enable tracking of the object. The activation mechanism may further include an initially closed circuit, and the activation mechanism may be configured to automatically activate the tracking tag when the tracking tag is removed from the liner material
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
G01S 1/68 - Marker, boundary, call-sign, or like beacons transmitting signals not carrying directional information
G06K 19/077 - Constructional details, e.g. mounting of circuits in the carrier
H04W 4/029 - Location-based management or tracking services
Aspects of the disclosure provide systems including linear material and tracking tags. A first tracking tag may be at least initially arranged on the liner material. The first tracking tag may also include beacon transmission circuitry including one or more batteries, a top layer, a bottom layer including an adhesive, and an activation mechanism. The activation mechanism may be configured to activate the first tracking tag and cause the beacon transmission circuitry to transmit beacon signals in order to enable tracking of the object. The activation mechanism may further include an initially closed circuit that extends beyond a perimeter of the top layer, and the activation mechanism may be configured to automatically activate the tracking tag when the tracking tag is separated from an adjacent tracking tag on the liner material
G06K 19/077 - Constructional details, e.g. mounting of circuits in the carrier
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for combining a simulator and neural network for predictions, such as predictions of Harmful Algal Bloom (HAB). One of the methods includes obtaining an estimate of a first number of marine-life cells representative of growth of the marine-life within a first region; providing the estimate to a prediction system that comprises (i) an ocean simulator and (ii) a trained network model, wherein the trained network model is trained to provide an indication of marine-life change using output from the ocean simulator; obtaining output from the prediction system, wherein the output indicates a second number of cells representative of growth of the marine-life within a second region; comparing the output to an output threshold; and in response to the output satisfying the output threshold, performing an action.
The technology relates to a wireless system that can be used indoors or outdoors, and is configured to reduce interference of beacon signals on channels used by the system. Aspects of the technology provide for evaluation of channel activity to determine an optimal transmission channel. This is beneficial where there is a high density of tags that may be configured for data transmission. Tags may include an antenna to receive signals; a first conditioning element to attenuate received signals corresponding with the system channels; a converter and a second conditioning element to prepare attenuated signals for analysis; a comparator to compare an attenuated signal to a threshold value; and a processor to transmit a beacon signal to a reader apparatus based on the comparison.
G06K 7/10 - Methods or arrangements for sensing record carriers by corpuscular radiation
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
33.
SIMULATING ELECTRICAL GRID TRANSMISSION AND DISTRIBUTION USING MULTIPLE SIMULATORS
Methods, systems, and apparatus, including medium-encoded computer program products, for selecting, from a unified electrical power grid model, a first proper subset of elements of the model. A first electrical grid simulation model is selected. A second, differing proper subset of elements of the model are selected. A second electrical grid simulation model that differs from the first electrical grid simulation model is selected. A set of boundary conditions common to the first electrical grid simulation model and the second electrical grid simulation model are determined. Operation of the electrical power grid is simulated, and can include (i) simulating, using the first electrical grid simulation model, the first proper subset of elements of the unified model, and the set of boundary conditions; and (ii) simulating, using the second electrical grid simulation model and the second proper subset of elements of the unified model, and the set of boundary conditions.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Aspects of the disclosure provide tracking tags 102, 104, 410, 414, 500, 1 100, 1 100', 1500, 1600. As an example, a tracking tag 102, 104, 410, 414, 500, 1 100, 1 100s, 1500, 1600 may include beacon transmission circuitry 530 including one or more batteries 710, 1 120, a frame 520, 1520 configured to hold the one or more batteries in place, an adhesive 540, 1570 arranged to secure the tracking tag to an object, and an activation mechanism configured to activate the tracking tag and cause the beacon transmission circuitry to transmit beacon signals in order to enable tracking of the object.
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
G06K 19/077 - Constructional details, e.g. mounting of circuits in the carrier
Aspects of the disclosure provide systems including linear material 560 and tracking tags 500, 580, 582, 584. The tracking tag may be at least initially arranged on the liner material. A first tracking tag 500 may also include beacon transmission circuitry 520 including one or more batteries 530, a top layer 510, a bottom layer 550 including an adhesive, and an activation mechanism. The activation mechanism may be configured to activate the first tracking tag and cause the beacon transmission circuitry to transmit beacon signals in order to enable tracking of the object. The activation mechanism may further include an initially closed circuit 570 that extends beyond a perimeter of the top layer, and the activation mechanism may be configured to automatically activate the tracking tag when the tracking tag is separated from an adjacent tracking tag 582 on the liner material.
G06K 7/10 - Methods or arrangements for sensing record carriers by corpuscular radiation
G06K 17/00 - Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups , e.g. automatic card files incorporating conveying and reading operations
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
G01S 1/04 - Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith using radio waves - Details
Aspects of the disclosure provide tracking tags. As an example, a tracking tag may include beacon transmission circuitry including one or more batteries, a frame configured to hold the one or more batteries in place, an adhesive arranged to secure the tracking tag to an object, and an activation mechanism configured to activate the tracking tag and cause the beacon transmission circuitry to transmit beacon signals in order to enable tracking of the object.
G06K 19/077 - Constructional details, e.g. mounting of circuits in the carrier
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
37.
MICROPLASTICS DETECTOR SENSOR COUPLING AND DATA TRAINING
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for receiving sensor data and refining a training model for microplastics detector. In some implementations, an exemplary method includes receiving microplastics detection data from a microplastics detection sensor and additional sensor data from one or more other sensors; providing the microplastics detection data and additional sensor data to a model trained to detect microplastics; receiving one or more values representing the amount of microplastics from the microplastics detection data and additional sensor data; and providing a representation of the one or more values for output of the model describing the amount of microplastics for use by one or more user devices.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
38.
TRAINING MACHINE LEARNING MODELS WITH SPARSE INPUT
This disclosure describes a system and method for effectively training a machine learning model to identify features in DAS and/or seismic imaging data with limited or no human labels. This is accomplished using a masked autoencoder (MAE) network that is trained in multiple stages. The first stage is a self-supervised learning (SSL) stage where the model is generically trained to predict data that has been removed (masked) from an original dataset. The second stage involves performing additional predictive training on a second dataset that is specific to a particular geographic region, or specific to a certain set of desired features. The model is fine-tuned using labeled data in order to develop feature extraction capabilities.
An underwater camera system includes a camera assembly configured to scan a seabed while submerged under water and moving in a direction of travel. A buoyant support is coupled to the camera assembly and configured to position the camera assembly under water during the moving in the direction of travel. A stabilization assembly is coupled to the camera assembly and configured for adjusting an orientation of the camera assembly relative to the direction of travel.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting features of an aquatic ecosystem. One of the methods includes generating, using ground truth data, first training input, wherein the first training input includes training labels; generating an augmented dataset from multiple data sources as second training input, wherein the augmented dataset is generated using (i) bathymetric data and (ii) simulated data based on satellite data indicating one or more coastal ecosystems; and training the machine learning model using (i) the first training input and (ii) second training input, such that the machine learning model is trained to predict biomass growth.
In an aspect, there is provided a method that includes: obtaining a physics-based ocean simulation model that specifies multiple simulation parameters, obtaining data characterizing an ocean in a geographical region, determining initial values of the simulation parameters based on data characterizing the ocean in the geographical region, and determining adjusted values of the simulation parameters. Determining adjusted values can include performing a simulation using the initial values of the simulation parameters to determine an initial simulation output, evaluating a task-specific objective function that is based on a measure of disagreement between: (i) the initial simulation output, and (ii) a target value for the ocean in the geographical region, and determining adjusted values of the simulation parameters of the physics-based ocean simulation model.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
42.
METASTRUCTURED PHOTONIC DEVICES FOR BINARY TREE MULTIPLEXING OR DEMULTIPLEXING OF OPTICAL SIGNALS
Photonic devices, photonic integrated circuits, optical elements, and techniques of making and using the same are described. A photonic device includes an input region adapted to receive an optical signal including a multiplexed channel characterized by a distinct wavelength, a dispersive region optically coupled with the input region to receive the optical signal, the dispersive region including a plurality of sub-regions defined by an inhomogeneous arrangement of a first material and a second material, and a plurality of output regions optically coupled with the input region via the dispersive region. The plurality of sub-regions can include an input channel section, one or more coupler sections, and one or more branching sections. The plurality of sub-regions together can configure the photonic device to demultiplex the optical signal and to isolate the multiplexed channel at a first output region of the plurality of output regions.
H04J 14/02 - Wavelength-division multiplex systems
G02B 6/293 - Optical coupling means having data bus means, i.e. plural waveguides interconnected and providing an inherently bidirectional system by mixing and splitting signals with wavelength selective means
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for Measuring and Quantifying Biodiversity in an Environment. One of the methods includes receiving a set of images representing the marine environment; identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment; classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.
G06V 10/74 - Image or video pattern matching; Proximity measures in feature spaces
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
A method for predicting growth of marine-life cells of a particular type, the method comprising: obtaining an estimate of a first number of marine-life cells representative of growth of the marine-life within a first region; providing the estimate to a prediction system that comprises (i) an ocean simulator and (ii) a trained network model, wherein the trained network model is trained to provide an indication of marine-life change using output from the ocean simulator; obtaining output from the prediction system, wherein the output indicates a second number of cells representative of growth of the marine-life within a second region; comparing the output to an output threshold; and in response to the output satisfying the output threshold, performing an action.
Methods, systems, and apparatus, including medium-encoded computer program products, for obtaining a plurality of images from at least one imaging device in an aquaculture environment and determining a statistical distribution of the livestock in the aquaculture environment from the plurality of images. Based on the statistical distribution, a location of a thermocline in the aquaculture environment can be determined. A signal indicative of the location of the thermocline can be provided to an aquaculture management device in the aquaculture environment.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for Measuring and Quantifying Biodiversity in an Environment. One of the methods includes receiving a set of images representing the marine environment; identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment; classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/58 - Extraction of image or video features relating to hyperspectral data
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for aquatic biomass estimation. One of the methods includes obtaining an image of an aquatic environment including aquatic grass; providing the image to a network model trained to construct a point cloud indicating a portion of the image that represents the aquatic grass; generating a floor model indicating a floor of the aquatic environment where the aquatic grass grows; identifying, using (i) the floor model and (ii) the point cloud indicating the aquatic grass, (i) a first subset of points in the point cloud as indicating aquatic grass and (ii) a second subset of points in the point cloud as indicating the floor of the aquatic environment; and generating, using the first subset of points in the point cloud, an indication of biomass within the aquatic environment.
Photonic devices, photonic integrated circuits, optical elements, and techniques of making and using the same are described. A photonic device includes an input region adapted to receive an optical signal including a multiplexed channel characterized by a distinct wavelength, a dispersive region optically coupled with the input region to receive the optical signal, the dispersive region including a plurality of sub-regions defined by an inhomogeneous arrangement of a first material and a second material, and a plurality of output regions optically coupled with the input region via the dispersive region. The plurality of sub-regions can include an input channel section, one or more coupler sections, and one or more branching sections. The plurality of sub-regions together can configure the photonic device to demultiplex the optical signal and to isolate the multiplexed channel at a first output region of the plurality of output regions.
In an aspect, there is provided a method (100) that includes: obtaining a physics-based ocean simulation model (102) that specifies multiple simulation parameters, obtaining data characterizing an ocean in a geographical region, determining initial values of the simulation parameters based on data characterizing the ocean in the geographical region, and determining adjusted values of the simulation parameters (104). Determining adjusted values can include performing a simulation using the initial values of the simulation parameters to determine an initial simulation output, evaluating a task-specific objective function that is based on a measure of disagreement between: (i) the initial simulation output, and (ii) a target value for the ocean in the geographical region, and determining adjusted values of the simulation parameters of the physics-based ocean simulation model (104).
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
G01V 99/00 - Subject matter not provided for in other groups of this subclass
Methods, systems, and apparatus, including medium-encoded computer program products, for obtaining a plurality of images from at least one imaging device in an aquaculture environment and determining a statistical distribution of the livestock in the aquaculture environment from the plurality of images. Based on the statistical distribution, a location of a thermocline in the aquaculture environment can be determined. A signal indicative of the location of the thermocline can be provided to an aquaculture management device in the aquaculture environment.
An underwater camera system includes a camera assembly configured to scan a seabed while submerged under water and moving in a direction of travel. A buoyant support is coupled to the camera assembly and configured to position the camera assembly under water during the moving in the direction of travel. A stabilization assembly is coupled to the camera assembly and configured for adjusting an orientation of the camera assembly relative to the direction of travel.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
B63B 79/15 - Monitoring properties or operating parameters of vessels in operation using sensors, e.g. pressure sensors, strain gauges or accelerometers for monitoring environmental variables, e.g. wave height or weather data
An optical modulator includes a modulation region, input, output, and sink ports, and a modulation actuator. The modulation region includes an inhomogeneous arrangement of two or more different materials having different refractive indexes. The input port is optically coupled to the modulation region to inject an optical carrier wave into the modulation region. The output port is optically coupled to the modulation region to receive and emit a modulated signal having a high state and a low state. The sink port is optically coupled to the modulation region. The modulation actuator is disposed proximate to the modulation region and adapted to apply a modulation bias to the modulation region that influences the different refractive indexes of the inhomogeneous arrangement to selectively steer a portion of optical power of the optical carrier wave to the sink port when the modulated signal is modulated into the low state.
G02F 1/025 - Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour based on semiconductor elements with at least one potential jump barrier, e.g. PN, PIN junction in an optical waveguide structure
54.
METASTRUCTURED PHOTONIC DEVICES FOR MULTIPLEXING OR DEMULTIPLEXING OF OPTICAL SIGNALS
Photonic devices, photonic integrated circuits, optical elements, and techniques of making and using the same are described. A photonic device includes an input region adapted to receive an optical signal including a multiplexed channel characterized by a distinct wavelength, a dispersive region optically coupled with the input region to receive the optical signal, the dispersive region including a plurality of sub-regions defined by an inhomogeneous arrangement of a first material and a second material, and a plurality of output regions optically coupled with the input region via the dispersive region. The plurality of sub-regions can include an input channel section, an in-coupler section, a parallel channel section, an out-coupler section, and an output channel section. The plurality of sub-regions together can configure the photonic device to demultiplex the optical signal and to isolate the multiplexed channel at a first output region of the plurality of output regions.
G02B 6/12 - Light guides; Structural details of arrangements comprising light guides and other optical elements, e.g. couplings of the optical waveguide type of the integrated circuit kind
55.
METASTRUCTURED PHOTONIC DEVICES FOR MULTIPLEXING OR DEMULTIPLEXING OF OPTICAL SIGNALS
Photonic devices, photonic integrated circuits, optical elements, and techniques of making and using the same are described. A photonic device includes an input region adapted to receive an optical signal including a multiplexed channel characterized by a distinct wavelength, a dispersive region optically coupled with the input region to receive the optical signal, the dispersive region including a plurality of sub-regions defined by an inhomogeneous arrangement of a first material and a second material, and a plurality of output regions optically coupled with the input region via the dispersive region. The plurality of sub-regions can include an input channel section, an in-coupler section, a parallel channel section, an out-coupler section, and an output channel section. The plurality of sub-regions together can configure the photonic device to demultiplex the optical signal and to isolate the multiplexed channel at a first output region of the plurality of output regions.
An optical modulator includes a modulation region, input, output, and sink ports, and a modulation actuator. The modulation region includes an inhomogeneous arrangement of two or more different materials having different refractive indexes. The input port is optically coupled to the modulation region to inject an optical carrier wave into the modulation region. The output port is optically coupled to the modulation region to receive and emit a modulated signal having a high state and a low state. The sink port is optically coupled to the modulation region. The modulation actuator is disposed proximate to the modulation region and adapted to apply a modulation bias to the modulation region that influences the different refractive indexes of the inhomogeneous arrangement to selectively steer a portion of optical power of the optical carrier wave to the sink port when the modulated signal is modulated into the low state.
G02F 1/01 - Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour
57.
Learned fabrication constraints for optimizing physical devices
A computer-implemented method for modeling fabrication constraints of a fabrication process is described. The method includes receiving training data including pre-fabrication structures and post-fabrication, training a fabrication constraint model by optimizing parameters of the fabrication constraint model based on the training data to model the fabrication constraints of the fabrication process, receiving an input design corresponding to a physical device, and generating a fabricability metric of the input design via the fabrication constraint model. The fabricability metric is related to a probabilistic certainty that the input design is fabricable by the fabrication process determined by the fabrication constraint model.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 30/23 - Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
Methods, systems, and apparatus for developing recipes for concrete mixtures are disclosed. A method includes obtaining second input data including a second recipe for combining the plurality of ingredients; mixing the plurality of ingredients according to the second recipe to produce a second mixture; obtaining sensor data representing one or more qualities of the second mixture; evaluating the second mixture using the sensor data to obtain performance measures of the concrete mixture; processing the input data with the mixture prediction model to obtain a corresponding output of the mixture prediction model, the corresponding output including predicted performance measures for the second mixture; and adjusting parameters of the mixture prediction model based on comparing the output of the mixture prediction model to the performance measures of the second mixture.
G16C 60/00 - Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
C04B 40/00 - Processes, in general, for influencing or modifying the properties of mortars, concrete or artificial stone compositions, e.g. their setting or hardening ability
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining an image of a fish captured by an underwater camera; identifying portions of the image corresponding to one or more areas of interest; extracting the portions of the image from the image; providing the portions of the image to a model trained to detect objects in the portions of the image; and determining an action based on output of the model indicating a number of object detections.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic synthetic seismic data items. One of the methods includes obtaining a plurality of synthetic seismic data items; obtaining a plurality of real seismic data items; processing each of the plurality of synthetic seismic data items using a machine learning model; processing each of the plurality of real seismic data items using the same machine learning model; determining a range for values for one or more parameters of a synthetic seismic data generator by comparing the synthetic seismic data items and the real seismic data items in an embedding space of the machine learning model; and selecting, as realistic synthetic seismic data items, a plurality of synthetic seismic data items that have been generated with a respective combination of values for the one or more parameters that is within the determined range.
G01V 1/28 - Processing seismic data, e.g. analysis, for interpretation, for correction
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining an image of a fish captured by an underwater camera; identifying portions of the image corresponding to one or more areas of interest; extracting the portions of the image from the image; providing the portions of the image to a model trained to detect objects in the portions of the image; and determining an action based on output of the model indicating a number of object detections.
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
Methods and systems for developing recipes for concrete mixtures are disclosed. A method includes obtaining input data, including characterization data (602) for the plurality of concrete mixture ingredients, recipe data (6006) for combining them and environmental data (612); mixing the plurality of ingredients according to the recipe to produce a concrete mixture (520); obtaining sensor data (510) representing one or more qualities of the concrete mixture (520); evaluating the concrete mixture using the sensor data (510) to obtain performance measures (516) of the concrete mixture; processing the input data with a mixture prediction model (620) to obtain a corresponding output of the mixture prediction model (620), the corresponding output including predicted performance measures (614) for the concrete mixture; and adjusting parameters of the mixture prediction model (620) based on comparing the output of the mixture prediction model to the performance measures (516) of the concrete mixture.
B28C 7/04 - Supplying or proportioning the ingredients
G16C 60/00 - Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic synthetic seismic data items. One of the methods includes obtaining a plurality of synthetic seismic data items; obtaining a plurality of real seismic data items; processing each of the plurality of synthetic seismic data items using a machine learning model; processing each of the plurality of real seismic data items using the same machine learning model; determining a range for values for one or more parameters of a synthetic seismic data generator by comparing the synthetic seismic data items and the real seismic data items in an embedding space of the machine learning model; and selecting, as realistic synthetic seismic data items, a plurality of synthetic seismic data items that have been generated with a respective combination of values for the one or more parameters that is within the determined range.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing recycled concrete aggregate (RCA). A method includes obtaining an aqueous carbonate solution by exposing an aqueous alkaline solution to a carbon dioxide laden fluid; performing a treatment process on a first portion of RCA particles using a first set of parameters, the treatment process including exposing the first portion of RCA particles to the aqueous carbonate solution; after performing the treatment process, obtaining measurements of the first portion of RCA particles; determining, using the measurements of the first portion of RCA particles, a second set of parameters; and performing the treatment process on a second portion of RCA particles using the second set of parameters. Exposing the first portion of RCA particles to the aqueous carbonate solution includes soaking the first portion of RCA particles in the aqueous carbonate solution.
This application relates to an actuator for an assistive device that includes: an upper and a lower attachment portion, a first drive pulley coupled to the upper attachment portion and having a first diameter, a second drive pulley coupled to the upper attachment portion and having a second diameter that is smaller than the first diameter, an idler pulley coupled to the lower attachment portion, and a belt that forms a loop by extending from the first drive pulley, around the idler pulley, and onto the second drive pulley, where activating a motor to rotate in a first direction causes the belt to be wound onto the first drive pulley and off the second drive pulley, and activating the motor to rotate in a second direction causes the belt to be wound off the first drive pulley and onto the second drive pulley.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing recycled concrete aggregate (RCA). A method includes obtaining an aqueous carbonate solution by exposing an aqueous alkaline solution to a carbon dioxide laden fluid; performing a treatment process on a first portion of RCA particles using a first set of parameters, the treatment process including exposing the first portion of RCA particles to the aqueous carbonate solution; after performing the treatment process, obtaining measurements of the first portion of RCA particles; determining, using the measurements of the first portion of RCA particles, a second set of parameters; and performing the treatment process on a second portion of RCA particles using the second set of parameters. Exposing the first portion of RCA particles to the aqueous carbonate solution includes soaking the first portion of RCA particles in the aqueous carbonate solution.
Methods, systems, and apparatus, including medium-encoded computer program products, for cloud-based electrical grid component validation can include obtaining a computer model of an electric power grid. The computer model can include asset models for individual assets connected to the electric power grid. Each asset model can be configured to replicate operation of a corresponding type of physical electric grid asset. A first asset model can include a hardware emulator configured to execute firmware specific to the corresponding type of physical electric grid asset. Altered firmware for the first asset model can be received. The computer model can be processed using a simulation engine to obtain simulation results, and the simulation engine can be configured to simulate operation of the individual assets of the computer model including operation of the first asset model according to the altered firmware associated with the first asset model. Simulation results can be provided.
A system includes: an ultrasound array comprising a plurality of transducer elements, wherein: a first subset of the plurality of transducer elements are configured to emit ultrasound pulses through the subject's skull for performing a neuro-modulation of the subject's brain, and a second subset of the plurality of transducer elements are configured to receive ultrasound signals from the subject's skull and brain in response to the ultrasound pulses being emitted from the first subset of the plurality of transducer elements; and a controller coupled to the ultrasound array, wherein the controller is configured to: generate at least one image depicting at least a portion of the subject's skull and brain based on, at least in part, the ultrasound signals received by the second subset of the plurality of transducer elements, and adapt the neuro-modulation of the subject's brain based on, at least in part, the at least one image.
A system includes: a form factor device sized and shaped to accommodate a subject's skull; an ultrasound array comprising a plurality of transducer elements attached to the form factor device, wherein the plurality of transducer elements are configured to: emit ultrasound pulses through the subject's skull for performing a neuro-modulation of the subject's brain during use of the system, and receive ultrasound signals from the subject's skull and brain in response to the ultrasound pulses being emitted; and a controller coupled to the ultrasound array, wherein the controller is configured, during use of the system, to: generate at least one image depicting at least a portion of the subject's skull and brain based on, at least in part, the received ultrasound signals received, and adapt the neuro-modulation of the subject's brain based on, at least in part, the at least one image during use of the system.
A system includes: an ultrasound array comprising a first subset of transducer elements are configured to emit ultrasound pulses through the subject's skull for performing a neuro-modulation of the subject's brain during use of the system, and a second subset of transducer elements configured to receive ultrasound signals from the subject's skull and brain in response to the ultrasound pulses being emitted from the first subset of transducer elements; and a controller coupled to the ultrasound array, wherein the controller is configured, during use of the system, to: detect a change of a physiological parameter in at least a portion of the subject's brain based on, at least in part, the ultrasound signals received by the second subset of transducer elements, and determine a dose of the neuro-modulation of the subject's brain based on, at least in part, the detected change of the physiological parameter.
A61B 8/08 - Detecting organic movements or changes, e.g. tumours, cysts, swellings
A61B 8/00 - Diagnosis using ultrasonic, sonic or infrasonic waves
G01S 15/89 - Sonar systems specially adapted for specific applications for mapping or imaging
G01S 7/52 - RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - Details of systems according to groups , , of systems according to group
Methods, systems, and apparatus, including medium-encoded computer program products, for cloud-based electrical grid component validation can include obtaining a computer model of an electric power grid. The computer model can include asset models for individual assets connected to the electric power grid. Each asset model can be configured to replicate operation of a corresponding type of physical electric grid asset. A first asset model can include a hardware emulator configured to execute firmware specific to the corresponding type of physical electric grid asset. Altered firmware for the first asset model can be received. The computer model can be processed using a simulation engine to obtain simulation results, and the simulation engine can be configured to simulate operation of the individual assets of the computer model including operation of the first asset model according to the altered firmware associated with the first asset model. Simulation results can be provided.
G06F 30/331 - Design verification, e.g. functional simulation or model checking using simulation with hardware acceleration, e.g. by using field programmable gate array [FPGA] or emulation
H02J 3/00 - Circuit arrangements for ac mains or ac distribution networks
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 111/02 - CAD in a network environment, e.g. collaborative CAD or distributed simulation
A magnetometer includes a sample signal device; a reference signal device; a microwave field generator operable to apply a microwave field to the sample signal device and the reference signal device; an optical source configured to emit light including light of a first wavelength that interacts optically with the sample signal device and with the reference signal device; at least one photodetector arranged to detect a sample photoluminescence signal including light of a second wavelength emitted from the sample signal device and a reference photoluminescence signal including light of the second wavelength emitted from the reference signal device, in which the first wavelength is different from the second wavelength; and a magnet arranged adjacent to the sample signal device and the reference signal device.
G01R 33/32 - Excitation or detection systems, e.g. using radiofrequency signals
G01N 24/10 - Investigating or analysing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using electron paramagnetic resonance
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
73.
FUNCTIONALIZED MATERIALS FOR CARBON CAPTURE AND SYSTEMS THEREOF
The present disclosure relates to a functionalized material, which may optionally be employed as a sorbent, as well as methods of making such materials and systems of using such materials. The processes, methods, and systems herein can be used for the separation of carbon dioxide from fluid streams.
B01J 20/10 - Solid sorbent compositions or filter aid compositions; Sorbents for chromatography; Processes for preparing, regenerating or reactivating thereof comprising inorganic material comprising silica or silicate
B01J 20/22 - Solid sorbent compositions or filter aid compositions; Sorbents for chromatography; Processes for preparing, regenerating or reactivating thereof comprising organic material
B01D 53/08 - Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases or aerosols by adsorption, e.g. preparative gas chromatography with moving adsorbents according to the "moving bed" method
This specification is generally directed to techniques for robust natural language (NL) based control of computer applications. In many implementations, the NL control is at least selectively interactive in that the user feedback input is solicited, and received, in resolving action(s), resolving action set(s), generating domain specific knowledge, and/or in providing feedback on implemented action set(s). The user feedback input can be utilized in further training of machine learning model(s) utilized in the NL based control of the computer applications.
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
75.
QUANTUM REPEATER FROM QUANTUM ANALOG-DIGITAL INTERCONVERTER
Quantum repeater systems and apparatus for quantum communication. In one aspect, a system includes a quantum signal receiver configured to receive a quantum field signal; a quantum signal converter configured to: sample quantum analog signals from a quantum field signal received by the quantum signal receiver; encode sampled quantum analog signals as corresponding digital quantum information in one or more qudits, comprising applying a hybrid analog-digital encoding operation to each quantum analog signal and a qudit in an initial state; decode digital quantum information stored in the one or more qudits as a recovered quantum field signal, comprising applying a hybrid digital-analog decoding operation to each qudit and a quantum analog register in an initial state; a quantum memory comprising qudits and configured to store digital quantum information encoded by the quantum signal converter; and a quantum signal transmitter configured to transmit the recovered quantum field signal.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for tracking object movement within a three-dimensional workspace. A method includes receiving, by a controller, break beam sensor data indicating object detection by a break beam sensor of a plurality of break beam sensors configured to detect objects within the workspace. The method includes receiving plate sensor data indicating object detection by a plate sensor of a plurality of plate sensors configured to detect objects resting on a surface of a plate defining a floor of the workspace. The method includes determining that an object passed through the workspace to rest at a position on the surface; comparing the position of the object to a target position of the object; and in response to determining that the position of the object does not satisfy similarity criteria for matching the target position, performing one or more actions.
A63H 33/04 - Building blocks, strips or similar building parts
A63H 33/08 - Building blocks, strips or similar building parts to be assembled without the use of additional elements provided with complementary holes, grooves, or protuberances, e.g. dovetails
Methods, systems, and apparatus for training a machine-learned model using satellite imagery and physical river gauge data as ground-truth information. Methods include receiving, from a user in a graphical user interface presented on a user device, a depth request for depth information at a geolocation. At least two satellite images are received, including the geolocation where a difference in respective capture times of each of the satellite images is within a threshold. The satellite images for the geolocation are provided to a machine-learned river gauge model. The machine-learned river gauge model determines depth information for the geolocation utilizing the satellite images, and provides, to the user in the graphical user interface, the depth information at the geolocation.
G06F 3/04815 - Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
09 - Scientific and electric apparatus and instruments
Goods & Services
(1) Electric sensors; Sensors for determining location, acceleration, proximity, motion, position, pressure, light, temperature, and humidity; Tracking sensors; Optical sensors; Sensors to record, measure, survey, process, track, and reproduce data, video, images, sounds, and measurements and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic communication devices using radio frequency or Bluetooth communications for tracking assets, packages, and cargo in transit; Electronic devices for viewing, processing, monitoring, tracking, managing, and providing information and predictive recommendations to customers on temperature, sensor location, inventory, freight and transportation logistics, location of cargo, packages and assets in transit, and warehouse and supply chain management; Temperature indicator labels, not for medical purposes; Data processing apparatus
09 - Scientific and electric apparatus and instruments
Goods & Services
(1) Electric sensors; sensors for determining location, acceleration, proximity, motion, position, pressure, light, temperature, and humidity; tracking sensors; optical sensors; sensors to record, measure, survey, process, track, and reproduce data, video, images, sounds, and measurements and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; electronic communication devices using radio frequency or Bluetooth communications for tracking assets, packages, and cargo in transit; electronic devices for viewing, processing, monitoring, tracking, managing, and providing information and predictive recommendations to customers on temperature, sensor location, inventory, freight and transportation logistics, location of cargo, packages and assets in transit, and warehouse and supply chain management; temperature indicator labels, not for medical purposes; data processing apparatus
Methods, systems, and apparatus, including medium-encoded computer program products, for predicting electrical component failure. A first sensor measurement of a component of an electrical grid taken at a first time can be obtained. A second sensor measurement of the component taken at a second time can be identified, and the second time can be after the first time. An input, which can include the first sensor measurement and the second sensor measurement, can be processed using a machine learning model that is configured to generate, based on one or more changes in one or more characteristics of the component as depicted in the second sensor measurement compared to the first sensor measurement, a prediction representative of a likelihood that the component will experience a type of failure during a time interval. Data indicating the prediction can be provided for presentation by a display.
G01N 29/14 - Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
This disclosure describes a system and method for generating images and location data of a subsurface object using existing infrastructure as a source. Many infrastructure objects (e.g., pipes, cables, conduits, wells, foundation structures) are constructed of rigid materials and have a known shape and location. Additionally these infrastructure objects can have exposed portions that are above or near the surface and readily accessible. A signal generator can be affixed to the exposed portion of the infrastructure object, which induces acoustic energy, or vibrations in the object. The object with affixed signal generator can then be used as a source in performing a subsurface imaging of subsurface objects, which are not exposed.
Disclosed implementations relate to automatically generating and providing guidance for navigating HCIs to carry out semantically equivalent/similar computing tasks across different computer applications. In various implementations, a domain of a first computer application that is operable using a first HCI may be used to select a domain model that translates between an action space of the first computer application and another space. Based on the selected domain model, a domain-agnostic action embedding—representing actions performed previously using a second HCI of a second computer application to perform a semantic task—may be processed to generate probability distribution(s) over actions in the action space of the first computer application. Based on the probability distribution(s), actions may be identified that are performable using the first computer application—these actions may be used to generate guidance for navigating the first HCI to perform the semantic task.
09 - Scientific and electric apparatus and instruments
Goods & Services
(1) Data processing apparatus; RFID readers; RFID readers to record, measure, survey, process, track, and reproduce data, video, images, sounds, measurements, location, acceleration, proximity, motion, position, pressure, temperature, and humidity, and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic devices for viewing, processing, monitoring, tracking, managing, and providing information and predictive recommendations to customers on temperature, sensor location, inventory, freight and transportation logistics, location of cargo, packages and assets in transit, and warehouse and supply chain management; Data collection devices, namely, optical readers;Wireless communication devices for voice, data, or image transmission; Computer hardware, namely, wireless access point (WAP) devices that emit, broadcast, or aggregate data from beacons; Computer networking hardware; Mobile computing and operating platforms consisting of data transceivers, wireless networks and gateways for collection and management of data; Scanners; Digital input and output scanners; Electric sensors; Sensors for determining location, acceleration, proximity, motion, position, pressure, light, temperature, and humidity; Tracking sensors; Optical sensors; Sensors to record, measure, survey, process, track, and reproduce data, video, images, sounds, and measurements and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic communication devices using radio frequency or Bluetooth communications for tracking assets, packages, and cargo in transit; Temperature indicator labels, not for medical purposes
84.
AGGREGATING INFORMATION FROM DIFFERENT DATA FEED SERVICES
Implementations are described herein for aggregating information responsive to a query from multiple different data feed services using machine learning. In various implementations, NLP may be performed on a natural language input comprising a query for information to generate a data feed-agnostic aggregator embedding (FAAE). A plurality of data feed services may be selected, each having its own data feed service action space that includes actions that are performable to access data via the data feed service. The FAAE may be processed based on domain-specific machine learning models corresponding to the selected data feed services. Each domain-specific machine learning model may translate between a respective data feed service action space and a data feed-agnostic semantic embedding space. Using these models, action(s) may be selected from the data feed service action spaces and performed to aggregate, from the plurality of data feed services, data that is responsive to the query.
Disclosed implementations relate to automatically generating and providing guidance for navigating HCIs to carry out semantically equivalent/similar computing tasks across different computer applications. In various implementations, a domain of a first computer application that is operable using a first HCI may be used to select a domain model that translates between an action space of the first computer application and another space. Based on the selected domain model, a domain-agnostic action embedding—representing actions performed previously using a second HCI of a second computer application to perform a semantic task—may be processed to generate probability distribution(s) over actions in the action space of the first computer application. Based on the probability distribution(s), actions may be identified that are performable using the first computer application—these actions may be used to generate guidance for navigating the first HCI to perform the semantic task.
Implementations are described herein for aggregating information responsive to a query from multiple different data feed services using machine learning. In various implementations, NLP may be performed on a natural language input comprising a query for information to generate a data feed-agnostic aggregator embedding (FAAE). A plurality of data feed services may be selected, each having its own data feed service action space that includes actions that are performable to access data via the data feed service. The FAAE may be processed based on domain-specific machine learning models corresponding to the selected data feed services. Each domain-specific machine learning model may translate between a respective data feed service action space and a data feed-agnostic semantic embedding space. Using these models, action(s) may be selected from the data feed service action spaces and performed to aggregate, from the plurality of data feed services, data that is responsive to the query.
09 - Scientific and electric apparatus and instruments
Goods & Services
Electric sensors; Sensors for determining location, acceleration, proximity, motion, position, pressure, light, temperature, and humidity; Tracking sensors; Optical sensors; Sensors to record, measure, survey, process, track, and reproduce data, video, images, sounds, and measurements and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic communication devices using radio frequency or Bluetooth communications for tracking assets, packages, and cargo in transit; Electronic devices for viewing, processing, monitoring, tracking, managing, and providing information and predictive recommendations to customers on temperature, sensor location, inventory, freight and transportation logistics, location of cargo, packages and assets in transit, and warehouse and supply chain management; Temperature indicator labels, not for medical purposes; Data processing apparatus.
09 - Scientific and electric apparatus and instruments
Goods & Services
Electric sensors; Sensors for determining location, acceleration, proximity, motion, position, pressure, light, temperature, and humidity; Tracking sensors; Optical sensors; Sensors to record, measure, survey, process, track, and reproduce data, video, images, sounds, and measurements and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic communication devices using radio frequency or Bluetooth communications for tracking assets, packages, and cargo in transit; Electronic devices for viewing, processing, monitoring, tracking, managing, and providing information and predictive recommendations to customers on temperature, sensor location, inventory, freight and transportation logistics, location of cargo, packages and assets in transit, and warehouse and supply chain management; Temperature indicator labels, not for medical purposes; Data processing apparatus.
09 - Scientific and electric apparatus and instruments
Goods & Services
Data processing apparatus; RFID readers; RFID readers to record, measure, survey, process, track, and reproduce data, video, images, sounds, measurements, location, acceleration, proximity, motion, position, pressure, temperature, and humidity, and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic devices for viewing, processing, monitoring, tracking, managing, and providing information and predictive recommendations to customers on temperature, sensor location, inventory, freight and transportation logistics, location of cargo, packages and assets in transit, and warehouse and supply chain management; Data collection devices, namely, optical readers;Wireless communication devices for voice, data, or image transmission; Computer hardware, namely, wireless access point (WAP) devices that emit, broadcast, or aggregate data from beacons; Computer networking hardware; Mobile computing and operating platforms consisting of data transceivers, wireless networks and gateways for collection and management of data; Scanners; Digital input and output scanners; Electric sensors; Sensors for determining location, acceleration, proximity, motion, position, pressure, light, temperature, and humidity; Tracking sensors; Optical sensors; Sensors to record, measure, survey, process, track, and reproduce data, video, images, sounds, and measurements and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic communication devices using radio frequency or Bluetooth communications for tracking assets, packages, and cargo in transit; Temperature indicator labels, not for medical purposes.
09 - Scientific and electric apparatus and instruments
Goods & Services
Data processing apparatus; RFID readers; RFID readers to record, measure, survey, process, track, and reproduce data, video, images, sounds, measurements, location, acceleration, proximity, motion, position, pressure, temperature, and humidity, and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic devices for viewing, processing, monitoring, tracking, managing, and providing information and predictive recommendations to customers on temperature, sensor location, inventory, freight and transportation logistics, location of cargo, packages and assets in transit, and warehouse and supply chain management; Data collection devices, namely, optical readers; Wireless communication devices for voice, data, or image transmission; Computer hardware, namely, wireless access point (WAP) devices that emit, broadcast, or aggregate data from beacons; Computer networking hardware; Mobile computing and operating platforms consisting of data transceivers, wireless networks and gateways for collection and management of data; Scanners; Digital input and output scanners; Electric sensors; Sensors for determining location, acceleration, proximity, motion, position, pressure, light, temperature, and humidity; Tracking sensors; Optical sensors; Sensors to record, measure, survey, process, track, and reproduce data, video, images, sounds, and measurements and wirelessly transmit the data, video, images, sounds, and measurements to a computer; GPS tracking devices; Electronic communication devices using radio frequency or Bluetooth communications for tracking assets, packages, and cargo in transit; Temperature indicator labels, not for medical purposes.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable software using artificial intelligence, machine learning, reinforcement learning, deep learning, and remote sensing for use in monitoring, tracking, managing, and providing information to customers on sensor location, inventory, freight logistics, transportation logistics, management of cargo, packages and assets in transit, warehouse management, supply chain management, waste reduction, healthcare services, and security and theft prevention services; Downloadable software using artificial intelligence, machine learning, reinforcement learning, deep learning, and remote sensing for use in inventory management, freight logistics management, supply chain management, warehouse management, healthcare services, waste reduction, management of cargo, packages and assets in transit, security and theft prevention services, and transportation logistics; Downloadable software for inventory management, freight logistics management, supply chain management, warehouse management, and transportation logistics; Downloadable software for providing information about the location of assets and about assets in transit, namely, vehicles, trailers, drivers, cargo and delivery containers; Downloadable software using geo-fencing technology to identify and track assets, inventory, and freight location; Downloadable computer simulation software for modeling transportation routes, weather conditions, asset deployment, asset utilization, purchasing patterns, and capital allocation; Downloadable software for providing information about the location, acceleration, proximity, motion, position, pressure, temperature, and humidity of assets and assets in transit Business advisory services in the field of transportation logistics, healthcare, business management of cargo, packages and assets in transit, security and theft prevention, inventory management, supply chain logistics, freight logistics, and delivery tracking; Business management consultation in the field of transportation logistics, healthcare, business management of cargo, packages and assets in transit, security and theft prevention, inventory management, supply chain logistics, freight logistics, and delivery tracking, product distribution, reverse logistics, and production systems and distribution solutions; Freight logistics management; Business management of cargo, packages and assets in transit; Transportation logistics services, namely, arranging the transportation of goods for others, warehouse management, and truck fleet management services; supply chain management services; Business consultation and advisory services in the field of transportation logistics, inventory management, supply chain logistics, freight logistics, delivery tracking, product distribution, operations management, logistics, reverse logistics, supply chain, and production systems and distribution solutions; Supply chain management services; Providing electronic tracking of freight information to others for business administration purposes; Providing tracking services and information concerning tracking of assets in transit, namely, vehicles, ships, trailers, drivers, cargo and delivery containers for business inventory purposes; Business management services, namely, providing information on assets and assets in transit; Business monitoring and consulting services, namely, providing strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for making predictive recommendations to customers Providing temporary use of on-line non-downloadable software using artificial intelligence, machine learning, reinforcement learning, deep learning, and remote sensing for use in monitoring, tracking, managing, and providing information predictive recommendations to customers on sensor location, inventory, freight logistics, transportation logistics, management of cargo, packages and assets in transit, warehouse management, and supply chain management; Providing temporary use of on-line non-downloadable software using artificial intelligence, machine learning, reinforcement learning, deep learning, and remote sensing for use in inventory management, freight logistics management, supply chain management, warehouse management, and transportation logistics; Providing temporary use of on-line non-downloadable software for use with inventory management, freight logistics management, supply chain management, warehouse management, and transportation logistics; Providing temporary use of on-line non-downloadable software for providing information about the location of assets and about assets in transit, namely, vehicles, trailers, drivers, cargo and delivery containers; Platform as a service (PaaS) services using artificial intelligence, machine learning, reinforcement learning, deep learning, and remote sensing for use in monitoring, tracking, managing, and providing information, and providing predictive recommendations to customers on sensor location, acceleration, proximity, motion, position, pressure, temperature, humidity, inventory, freight logistics, transportation logistics, management of cargo, packages and assets in transit, warehouse management, and supply chain management; Software as a service (SaaS) services using artificial intelligence, machine learning, reinforcement learning, deep learning, and remote sensing for use in monitoring, tracking, managing, and providing information, and providing predictive recommendations to customers on sensor location, acceleration, proximity, motion, position, pressure, temperature, humidity, inventory, freight logistics, transportation logistics, management of cargo, packages and assets in transit, warehouse management, and supply chain management; Consulting services in the design and implementation of computer-based information systems for businesses; Providing temporary use of on-line non-downloadable software using geo-fencing technology to identify and track assets, inventory, and freight location; Providing temporary use of on-line non-downloadable software for providing information about the location, acceleration, proximity, motion, position, pressure, temperature, and humidity of assets and assets in transit; Design of computer-simulated models; Computer modeling services; Providing temporary use of on-line non-downloadable computer simulation software for modeling transportation routes, weather conditions, cargo and package deployment, purchasing patterns, and capital allocation
92.
ELECTRICAL GRID OPERATIONS USING ON FORECAST HORIZON PLANNING
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for operating sources of electricity in an electrical grid having a forecasting error rate are described. In some implementations, a method includes selecting a forecast horizon based on the forecasting error rate; performing an optimization process for dispatch planning of the electrical grid for the selected forecast horizon; automatically operating one or more power sources of the electrical grid in accordance with the dispatch planning for a first time increment; and performing an optimization process for dispatch planning for subsequent time increments within the selected horizon after the first time increment has elapsed.
A technique for electrolysis includes applying a voltage across anode and cathode electrodes bathed in an electrolytic solution disposed within a plurality of hydrogen electrolyzer cells, venting hydrogen gas produced in cathode chambers of the hydrogen electrolyzer cells to a hydrogen exhaust manifold, venting oxygen gas produced in anode chambers of the hydrogen electrolyzer cells to an oxygen exhaust manifold, evaporating a portion of the electrolytic solution within at least one of the cathode or anode chambers, and maintaining the electrolytic solution in the hydrogen at least in part on an evaporative cooling of the electrolytic solution within the hydrogen electrolyzer cells.
Methods, systems, and apparatus, including medium-encoded computer program products, for receiving outputs from a plurality of models that are each informed by real-time data provided by one or more sensors that are present in an aquaculture environment. An input is generated for an algorithmic music composer for algorithmically composing music that reflects multiple current conditions within the aquaculture environment, based at least on the received outputs from the plurality of models. The input is provided to the algorithmic music composer to algorithmically compose the music that reflects the multiple current conditions within the aquaculture environment.
G10H 1/00 - ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE - Details of electrophonic musical instruments
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for setting modes for an underwater camera. In some implementations, a scheduler repeatedly: obtains data of a current context of an underwater camera; determines whether the current context satisfies first criteria associated with continued activation of one or more modes that are currently activated and satisfies second criteria associated with activation of one or more modes that are not currently activated; selects modes to be active based on (i) determining whether the current context satisfies the first criteria and (ii) determining whether the current context satisfies the second criteria; and activating any of the modes that are to be active and that are not currently activated, or deactivating any of the modes that are currently activated that are not included in the one or more modes that are to be activated.
Methods, systems, and apparatus, including medium-encoded computer program products, for predicting electrical component failure. A first sensor measurement of a component of an electrical grid taken at a first time can be obtained. A second sensor measurement of the component taken at a second time can be identified, and the second time can be after the first time. An input, which can include the first sensor measurement and the second sensor measurement, can be processed using a machine learning model that is configured to generate, based on one or more changes in one or more characteristics of the component as depicted in the second sensor measurement compared to the first sensor measurement, a prediction representative of a likelihood that the component will experience a type of failure during a time interval. Data indicating the prediction can be provided for presentation by a display.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correcting finite floating-point numerical simulation and optimization. Defining a loss function within a simulation space composed of a plurality of voxels each having an initial degree of freedom, the simulation space encompassing one or more interfaces of the component; defining an initial structure for the one or more interfaces in the simulation space; calculating, using a computer system with a finite floating-point precision, values for an electromagnetic field at each voxel using a finite-difference time domain solver to solve Maxwell's equations; and determining, for each voxel, whether to increase a respective numerical precision of respective values representing behavior of the electromagnetic field at the voxel above a threshold precision by the computer system and, in response, assigning one or more additional degrees of freedom to the voxel.
G06F 7/483 - Computations with numbers represented by a non-linear combination of denominational numbers, e.g. rational numbers, logarithmic number system or floating-point numbers
The technology enables locating asset tracking tags (102, 104, 414, 426) based on one or more beacon signals (412) from at least one anchor beacon (105, 410, 424). Each of the beacon signals (412) including anchor beacon identification information and being associated with a received signal strength upon receipt at a reader device (106, 416, 428). The anchor beacon identification information being associated with a physical location of the anchor beacon (105, 410, 424). A position of the reader device (106, 416, 428) is estimated according to the received signal strength of the one or more beacon signals (412) and the physical location of the at least one anchor beacon (105, 410, 424) from the anchor beacon identification information. One or more signals (412) from an asset tracking tag (102, 104, 414, 426) are detected by the reader device (106, 416, 428). A location of the asset tracking tag (102, 104, 414, 426) is identified based on the estimated position of the reader device (106, 416, 428) and signal strength information for each of the one or more detected signals from the asset tracking tag (102, 104, 414, 426).
G06K 7/10 - Methods or arrangements for sensing record carriers by corpuscular radiation
G06K 19/07 - Record carriers with conductive marks, printed circuits or semiconductor circuit elements, e.g. credit or identity cards with integrated circuit chips
G01S 11/06 - Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The technology enables locating asset tracking tags based on one or more beacon signals from at least one anchor beacon. Each of the beacon signals including anchor beacon identification information and being associated with a received signal strength upon receipt at a reader device. The anchor beacon identification information being associated with a physical location of the anchor beacon. A position of the reader device is estimated according to the received signal strength of the one or more beacon signals and the physical location of the at least one anchor beacon from the anchor beacon identification information. One or more signals from an asset tracking tag are detected by the reader device. A location of the asset tracking tag is identified based on the estimated position of the reader device and signal strength information for each of the one or more detected signals from the asset tracking tag.
Methods, systems, and apparatus, including medium-encoded computer program products, for receiving outputs from a plurality of models that are each informed by real-time data provided by one or more sensors that are present in an aquaculture environment. An input is generated for an algorithmic music composer for algorithmically composing music that reflects multiple current conditions within the aquaculture environment, based at least on the received outputs from the plurality of models. The input is provided to the algorithmic music composer to algorithmically compose the music that reflects the multiple current conditions within the aquaculture environment.
G10H 1/00 - ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE - Details of electrophonic musical instruments
