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.
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.
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
6.
FACILITATION OF SEQUENCE SELECTION BY USING ENCODING EFFICIENCY TO IDENTIFY ENCODER MODEL
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.
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 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
10.
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.
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
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
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
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 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.
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
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
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.
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.
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"
32.
DESIGNING PHOTONIC INTEGRATED CIRCUITS BASED ON ARRAYS OF INVERSE-DESIGNED COMPONENTS
In some embodiments, a method for designing a photonic device is provided. A design optimization system receives an initial design for the photonic device. The initial design includes one or more inputs, one or more outputs, a number of subcomponent regions, and a number of waveguides for connecting the subcomponent regions. The design optimization system simulates each subcomponent region to determine simulated s-parameters of each subcomponent region. The design optimization system determines overall s-parameters for a simulated photonic device based on the simulated s-parameters of each subcomponent region and s-parameters of the waveguides. The design optimization system determines an overall gradient associated with the overall s-parameters. The design optimization system optimizes one or more subcomponent regions based on the overall gradient to create an updated design for the photonic device.
G06F 30/398 - Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM]
G06F 30/3308 - Design verification, e.g. functional simulation or model checking using simulation
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
G06F 111/06 - Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
33.
MODEL-PREDICTIVE CONTROL OF PEST PRESENCE IN HOST ENVIRONMENTS
Systems and methods for controlling a population of a pest are provided. A computer implemented method for controlling a population of a pest can include receiving population data describing a presence of a pest in a host environment at a first time. The method can include receiving environmental data describing the host environment over a prediction horizon including and temporally after the first time. The method can include generating an intervention action for the first time using the population data and the environmental data as inputs to a control model configured to output the intervention action as part of an optimization of the presence of the pest over the prediction horizon. The method can also include outputting the intervention action.
A01M 1/02 - Stationary means for catching or killing insects with devices attracting the insects
A01M 1/00 - Stationary means for catching or killing insects
A01M 5/00 - Catching insects in fields, gardens, or forests by movable appliances
A01M 99/00 - Subject matter not provided for in other groups of this subclass
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 classifying an input time series into a class from a set of classes. In one aspect, a method comprises: receiving an input time series; processing the input time series using a reconstruction model to generate a reconstruction model output that comprises a plurality of channels, wherein each channel of the plurality of channels defines a respective output time series, and wherein each channel of the plurality of channels corresponds to a respective class from the set of classes; determining a respective reconstruction error for each channel of the plurality of channels based on an error between: (i) the output time series defined by the channel, and (ii) the input time series; and classifying the input time series as being included in a class from the set of classes based on the reconstruction errors.
As opposed to a rigid approach, implementations disclosed herein utilize a flexible approach in automatically determining an action set to utilize in attempting performance of a task that is requested by natural language input of a user. The approach is flexible at least in that embedding technique(s) and/or action model(s), that are utilized in generating action set(s) from which the action set to utilize is determined, are at least selectively varied. Put another way, implementations leverage a framework via which different embedding technique(s) and/or different action model(s) can at least selectively be utilized in generating different candidate action sets for given NL input of a user. Further, one of those action sets can be selected for actual use in attempting real-world performance of a given task reflected by the given NL input. The selection can be based on a suitability metric for the selected action set and/or other considerations.
G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction. In some implementations, an exemplary method includes obtaining one or more images of a population of fish captured by an underwater camera; providing data corresponding to the one or more images to a model trained to predict biomass values; obtaining output of the trained model including a predicted biomass value indicating a future biomass of a fish within the population of fish; and determining an action based on the predicted biomass value.
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 (102,104,400,500,600) that may be configured for data transmission. Tags (102,104,400,500,600) may include an antenna (440,540,640) to receive signals; a set of first conditioning elements (442a-c,542a-c,642) to attenuate received signals corresponding with system channels; a set of converters (444a-c,544a-c,644) and a set of second conditioning elements (446a-c,546a-c,646) to prepare attenuated signals for analysis; a comparator (448,548,648) to determine which attenuated signal corresponds to a channel having the lowest power level; and a processor (450,550,650) to transmit a beacon signal to a reader apparatus (106) on the channel with the lowest power level.
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
Disclosed implementations relate to automating semantically-similar computing tasks across multiple contexts. In various implementations, an initial natural language input and a first plurality of actions performed using a first computer application may be used to generate a first task embedding and a first action embedding in action embedding space. An association between the first task embedding and first action embedding may be stored. Later, subsequent natural language input may be used to generate a second task embedding that is then matched to the first task embedding. Based on the stored association, the first action embedding may be identified and processed using a selected domain model to select actions to be performed using a second computer application. The selected domain model may be trained to translate between an action space of the second computer application and the action embedding space.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for distribution-based machine learning. In some implementations, a method for distribution-based machine learning includes obtaining fish images from a camera device; generating predicted values using a machine learning model and one or more of the fish images; comparing the predicted values to distribution data representing features of multiple fish; and updating one or more parameters of the machine learning model based on the comparison.
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 a monocular underwater camera; providing the image of the fish to a depth perception model; obtaining output of the depth perception model indicating a depth-enhanced image of the fish data in the image; determining a biomass value estimate of the fish based on the output; and determining an action based on one or more biomass values estimates including the biomass value estimate of the fish.
The technology relates to free-space optical communication systems that correct for errors in tracking and pointing accuracy to maintain connection integrity. Such systems can both proactively and reactively correct for errors in tracking performance and pointing accuracy of terminals within the system. An aspect includes receiving information indicative of at least one external disturbance associated with a communication device (102, 122). A determination is made for a proactive estimation indicative of a first error associated with an effect of the at least one external disturbance at a current timestep. A determination is made for a reactive estimation indicative of a second error associated with the effect of the at least one external disturbance at a previous timestep. A final control signal is determined based on the proactive estimation and the reactive estimation. A controller (220) is able to actuate an optical assembly of the communication device based on the determined final control signal.
A hydrogen electrolyzer system generates hydrogen and oxygen gases via electrolysis. The hydrogen and oxygen gases are exhausted to hydrogen and oxygen exhaust manifolds, respectively. An absolute pressure in one of the hydrogen or oxygen exhaust manifolds is monitored. A differential pressure between the hydrogen and oxygen exhaust manifolds is monitored. Backpressures in the hydrogen and oxygen exhaust manifolds are controlled based upon the absolute and differential pressures.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining turbidity of water using machine learning. One of the methods includes obtaining, by a camera, an image of water; detecting, using a blob detector, a plurality of blobs in the image that represent particles suspended in the water; determining a distribution of the plurality of blobs; determining, from the distribution of the plurality of blobs, a measurement associated with turbidity of the water; and providing a signal associated with the measurement.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting, from a set of actions, actions to be performed by an agent interacting with an environment to cause the agent to perform a task. One of the methods includes receiving a current observation characterizing a current environment state of the environment, selecting an action to be performed by the agent in response to the current observation by performing multiple iterations of outer look ahead search, wherein performing the multiple iterations of outer look ahead search comprises, in each outer look ahead search iteration: determining a proper subset of the possible future states of the environment; determining that one or more inner look ahead search commencement criteria are satisfied; and in response, performing an inner look ahead search of the proper subset of the possible future states of the environment.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid model error reduction are enclosed. A method includes obtaining graph data defining a graph including nodes and edges. Each node represents a component of an electric grid and is associated with respective node data representing electrical properties of the component of the electric grid, each edge represents a connection between components of the electric grid and is associated with respective edge data representing electrical properties of the connection, and the graph data includes one or more errors, each error including erroneous node data or erroneous edge data. The method includes processing the graph data using an error-correcting model trained to correct errors in the graph data; obtaining, as output from the error-correcting model, output graph data; and verifying accuracy of the output graph data by processing the output graph data using an electric grid simulator.
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 30/20 - Design optimisation, verification or simulation
G06F 119/06 - Power analysis or power optimisation
A photonic coupler includes an input coupling section, an output coupling section, and a multimode interference (MMI) waveguide section. The input coupling section is adapted to receive an input optical signal along an input waveguide channel. The output coupling section is adapted to output a pair of output optical signals along output waveguide channels. The output optical signals having output optical powers split from the input optical signal. The MMI waveguide section is optically coupled between the input and output coupling sections. Notched waveguide sections may each be disposed between the MMI waveguide section and a corresponding one of the input or output coupling sections and/or the MMI waveguide section may include curvilinear sidewalls.
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
G02B 6/122 - Basic optical elements, e.g. light-guiding paths
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to perform a machine learning task. In one aspect, a method includes: obtaining a set of training examples; obtaining, for each training example, a respective metadata label that characterizes the training example; and training the machine learning model over a sequence of training stages, including, at each training stage: identifying a selection criterion corresponding to the current training stage that defines a criterion for selecting training examples based on the metadata labels of the training examples; selecting a proper subset of the set training examples as training data for the current training stage in accordance with the selection criterion for the current training stage; and updating the machine learning model by training the machine learning model on the training data for the current training stage.
Systems, devices, and methods for optimization of conducting interconnects are described. A method includes receiving an integrated circuit layout including a plurality of terminals and an interconnect, wherein the interconnect represents a conductive coupling between the plurality of terminals. The method includes receiving terminal information describing operating parameters of the plurality of terminals. The method includes receiving layer information describing material composition and material property information for the plurality of terminals and the interconnect. The method includes generating a three-dimensional representation of an integrated circuit using the integrated circuit layout and the layer information. The method includes determining an individual contribution of a cell included in the three-dimensional representation to a resistance-capacitance (RC) value of the interconnect using the three-dimensional representation and the terminal information. The method also includes generating an updated integrated circuit layout based at least in part on the individual contribution.
G06F 30/392 - Floor-planning or layout, e.g. partitioning or placement
H01L 27/02 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including integrated passive circuit elements with at least one potential-jump barrier or surface barrier
49.
AUTOMATED TRANSISTOR-LEVEL PLACEMENT FOR DESIGN OF INTEGRATED CIRCUITS
In some embodiments, a computer-implemented method for designing an integrated circuit using transistor placement optimization is provided. A computing system receives a specification for the integrated circuit. The specification includes a netlist describing a plurality of transistors and connections between terminals of the plurality of transistors. The computing system determines an initial location and an orientation on a canvas for each transistor in the plurality of transistors. The computing system uses an objective function based at least in part on the initial locations and the orientations of the plurality of transistors to generate a rough placement having globally optimized locations and orientations for the plurality of transistors. The computing system uses a local refinement technique to optimize the rough placement to generate a fine placement, and uses a routing technique to generate a routing for the fine placement to generate a completed design.
G06F 30/392 - Floor-planning or layout, e.g. partitioning or placement
G06F 30/398 - Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM]
G06F 30/327 - Logic synthesis; Behaviour synthesis, e.g. mapping logic, HDL to netlist, high-level language to RTL or netlist
G06F 30/33 - Design verification, e.g. functional simulation or model checking
H01L 27/02 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including integrated passive circuit elements with at least one potential-jump barrier or surface barrier
G06F 117/12 - Sizing, e.g. of transistors or gates
A set of conditions is defined that to be simulated via execution of a machine-learning model For each condition, a set of learnable condition-specific parameters is identified to configure a model architecture. A first learnable condition-specific parameter associated with a first condition of the set of conditions can be identified a shared or global parameter that is to have a same value as at least another learnable condition-specific parameter (associated with another condition). One or more parameter data structures can be configured with parameter values for the sets of condition-specific parameters for the sets of conditions, where the configuration imposes a constraint that a value for the first condition-specific parameter and the at least one value for the at least one other condition-specific parameter are the same. The machine-learning model can be trained using the configured parameter data structure(s).
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
51.
METHOD AND SYSTEM FOR USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK
Some implementations relate to using trained robotic action ML models in controlling a robot to perform a robotic task. Some versions of those implementations include (a) a first modality robotic action ML model that is used to generate, based on processing first modality sensor data instances, first predicted action outputs for the robotic task and (b) a second modality robotic action ML model that is used to generate, in parallel and based on processing second modality sensor data instances, second predicted action outputs for the robotic task. In some of those versions, respective weights for each pair of the first and second predicted action outputs are dynamically determined based on analysis of embeddings generated in generating the first and second predicted action outputs. A final predicted action output, for controlling the robot, is determined based on the weights.
Techniques for creating a design for a physical device (316) are disclosed. A computing system receives a design specification. The design specification includes a design region (332), one or more ports (302, 308, 310, 312, 314), and a port location perimeter. The computing system determines an initial proposed design based on the design specification that includes the design region and a location for each port of the one or more ports within the port location perimeter. The computing system optimises the design region of the initial proposed design to create an improved design region, and optimises at least one location of a port of the one or more ports (302, 308, 310, 312, 314) within the port location perimeter to create an improved proposed design.
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 (102) or the remote device; and receiving or detecting an apparent amount of alignment of the optical signal. Then, by one or more processors (104) of the first communication device (102), 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 (102) based on the actual amount of alignment to correct for optical cross coupling.
This disclosure describes a system and method for generating images by performing TEM surveys using pre-existing infrastructure such as transmission lines, or power lines, and naturally occurring transients such as lightning strikes or load switching. A relatively inexpensive sensor array can be installed on overhead power lines (e.g., electrical transmission or sub-transmission lines) which can detect transients in the overhead power lines. Transients in the overhead power lines can cause the power lines to emit pulses of electromagnetic (EM) radiation, which propagate into the earth's subsurface. This sudden change in electromagnetic field in the subsurface can induce eddy currents, which in turn emit return EM radiation that can propagate back to the overhead power line and induce secondary voltage and current transients. The magnitude of these secondary transients, and their time delay from the original transient are influenced by the properties of the subsurface in which the eddy currents formed.
G01V 3/08 - Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination or deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
55.
POWER CONTROL LOOP FOR STABILIZATION OF LINK POWER
The technology employs a state-based power control loop (PCL) architecture to maintain tracking and communication signal-to-noise ratios at suitable levels for optimal tracking performance and data throughput in a free-space optical communication system. Power for a link is adjustable to stay within a functional range of receiving sensors (118) in order to provide continuous service to users. This avoids oversaturation and possible damage to the equipment. The approach can include decreasing or increasing the power to counteract a surge or drop while maintaining a near constant received power at a remote communication device ( 130). The system may receive power adjustment feedback from another communication terminal (910) and perform state-based power control according to the received feedback (912). This can include re-initializing and reacquiring a link with the other communication terminal automatically after loss of power, without human intervention. There may be a default state and discrete states including rain (606), fade (610), surge (612) and unstable (608) states.
H04B 10/077 - Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems using an in-service signal using a supervisory or additional signal
H04B 10/112 - Line-of-sight transmission over an extended range
56.
MOUNT FOR A CALIBRATION TARGET FOR ULTRASONIC REMOVAL OF ECTOPARASITES FROM FISH
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for obtaining initial parameters for ultrasonic transducers around a calibration target. The calibration target can include a fish-shaped structure, sensors placed 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. The calibration target can be fixed at a particular position relative to the ultrasonic transducers by a filament coupled to both the calibration target and a support structure. Sensor data can be obtained from the calibration target at the particular position relative to the ultrasonic transducers, and relative locations of the sensors can be determined. Parameters for the ultrasonic transducers around the calibration target can be adjusted based on the sensor data and the respective locations of the sensors.
In some embodiments, a non-transitory computer-readable medium is provided. The computer-readable medium has logic stored thereon that, in response to execution by one or more processors of a computing system, cause the computing system to perform actions for deriving a fabrication model for a fabrication system using an inverse design process. The actions include determining a test design for a test physical device, measuring performance of an instance of the test physical device fabricated by the fabrication system using the test design to determine an as-fabricated performance metric, optimizing the test design using a first loss function based on differences in a simulated performance metric of the test design and the as-fabricated performance metric to determine an as-fabricated design, and optimizing a fabrication model using a second loss function based on differences between the test design and the as-fabricated 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
The technology enables locating asset tracking tags based on a ramped sequence of signals from one or more beacon tracking tags (102, 104). The sequence includes at least one minimum power signal and at least one maximum power signal. Each signal in the sequence has a tag identifier and an initial signal strength value. Each beacon signal in the ramped sequence is associated with the time at which that beacon signal was received by a reader. Each beacon signal is also associated with a received signal strength at reception. A location of the beacon tracking tag (102,104) is estimated according to the signals in the sequence based on the difference between the initial and received signal strengths. A position of the reader device (106) is identified based on the beacon tag's location. An asset tracking tag location is identified based on the reader's location and packets received by the reader from the asset tag (102,104).
H04W 4/029 - Location-based management or tracking services
H04W 52/36 - Transmission power control [TPC] using constraints in the total amount of available transmission power with a discrete range or set of values, e.g. step size, ramping or offsets
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for automatic object detection for underwater cameras. In some implementations, an underwater camera captures many images which are obtained by a control unit. The control unit can detect one or more contours within a captured image based on values representing pixels of the image, generate a representation of the image based on the detected contours, provide the representation to a model that is trained to classify an input image as including a net or as not including a net, and perform an action based on classifying the image as including a net.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
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 10/50 - Extraction of image or video features by summing image-intensity values; Projection analysis
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
60.
MULTILAYER PHOTONIC DEVICES WITH METASTRUCTURED LAYERS
A multilayer photonic device is described, including an input region configured to receive an input signal, a multilayer stack optically coupled with the input region to receive the input signal, and an output region optically coupled with the multilayer stack to output an output signal. The multilayer stack can include a first metastructured dispersive region disposed in a first patterned layer of the multilayer stack and a second metastructured dispersive region disposed in a second patterned layer of the multilayer stack and optically coupled with the first metastructured dispersive region. The first metastructured dispersive region and the second metastructured dispersive region can together structure the multilayer stack to generate the output signal in response to the input signal.
G02B 6/122 - Basic optical elements, e.g. light-guiding paths
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
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction aggregation. In some implementations, an exemplary method includes obtaining images of fish captured by an underwater camera; providing data of the images to a trained model; obtaining output of the trained model indicating the likelihoods that the biomass of fish are within multiple ranges; combining likelihoods of the output based on one or more ranges common to likelihoods of two or more fish to generate a biomass distribution; and determining an action based on the biomass distribution.
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater feed movement detection. In one aspect, the method may include the actions of obtaining images captured at different time points, where the images are captured by a camera and indicate feed that has been dispersed by a feeder for aquatic livestock inside an enclosure; determining, for each image, respective locations of the feed indicated by the image; determining, from the respective locations of the feed, a respective movement of the feed over the different time points; determining, based on the respective feed movement of the feed over the different time points, water current movement within the enclosure for the aquatic livestock; and outputting an indication of the water current movement.
Computer-implemented methods may include accessing a multi-dimensional embedding space that supports relating embeddings of molecules to predicted values of a given property of the molecules. The method may also include identifying one or more points of interest within the embedding space based on the predicted values. Each of the one or more points of interest may include a set of coordinate values within the multi dimensional embedding space and may be associated with a corresponding predicted value of the given property. The method may further include generating, for each of the one or more points of interest, a structural representation of a molecule by transforming the set of coordinate values included in the point of interest using a decoder network. The method may include outputting a result that identifies, for each of the one or more points of interest, the structural representation of the molecule corresponding to the point of interest.
Computer-implemented methods may include identifying a polymer for decomposition. The method may further include accessing, for an ionic liquid, one or more properties corresponding to the polymer. One or more properties may characterize a reaction between the polymer and the ionic liquid. The method may also include accessing a value of the property using a quantum-mechanical or thermodynamical method. The method may include determining a bond string and position (BSP) representation of a molecule of the ionic liquid. The method may further include determining an embedded representation of the ionic liquid based on the BSP representation. In addition, the method may include generating a relationship between BSP representations of molecules and the one or more properties. The method may also include identifying an ionic liquid as a prospect for depolymerizing the specific polymer based on the relationship. The method may include outputting an identification of the ionic liquid.
Bayesian optimization of depolymerization reactions in ionic liquids is disclosed. The purpose of the disclosed methods is to find ionic liquids that can be used to depolymerize plastics so as to facilitate plastic recycling. Methods may include accessing a first data set that includes a plurality of first data elements. Each of the plurality of first data elements may characterize a depolymerization reaction. Each first data element may include an embedded representation of a structure of a reactant and a reaction-characteristic value that characterizes a reaction between the reactant and a polymer. The embedded representation may be identified as a set of coordinate values within an embedding space. The method may include constructing a predictive function to predict reaction-characteristic values from embedded representations. The method may also include evaluating a utility function that transforms a given point within the embedding space into a utility metric. The method may include identifying particular points as corresponding to high utility metrics. The method may also include outputting a result that identifies a reactant corresponding to the particular point or a reactant structure corresponding to the particular point.
Computer-implemented methods may include accessing a predictive function. The predictive function may be configured to receive a partial or complete bond string and position (BSP) representation of a molecule of a reactant ionic liquid, where the representation identifies relative positions of atoms in the molecule. The predictive function may be configured to predict a reaction-characteristic value that characterizes a reaction between the ionic liquid and a particular polymer. The predictive function may be generated using training data corresponding to a set of molecules that were selected using Bayesian optimization, one or more previous versions of the predictive function, and experimentally derived reaction-characteristic values characterizing reactions between the molecules and the particular polymer. The method may also include identifying a particular ionic liquid as a prospect for depolymerizing the particular polymer based on the predictive function. The method may further include outputting an identification of the ionic liquid.
According to some embodiments of the present disclosure, an intelligent tracking system is disclosed. The intelligent tracking system, includes one or more passive tracking devices, an exciter, and a tracker. Each passive tracking device includes one or more transceivers and is energized by an electromagnetic frequency. In response to being energized each passive tracking device transmits a short message. The exciter emits the electromagnetic frequency. The tracker receives short messages from the one or more passive tracking devices and confirms the presence of the one or more passive tracking devices in a vicinity of the tracker based on the received messages.
Methods and systems including receiving a plurality of shipping bids from a plurality of shipping entities, each entity having goods to ship from locations to destinations, wherein each bid represents an option to ship goods at a shipping price, and wherein each bid comprises a plurality of shipping parameters; receiving a plurality of carrier bids from a plurality of carrier entities, each entity transporting the goods, wherein each bid represents an option to transport the goods at a price, and wherein each bid comprises a plurality of carrier parameters; performing a matching process to generate a plurality of pair-wise partial matches, wherein each match associates a shipping and carrier bid at a modified price, wherein the modified price is based on a deviation between the parameters; providing information representing the matches to the shipping and carrier entities; and generating training data representing which matches were exercised.
A sensing device is described for mounting on a movable component of a robotic device. The sensing device includes a plurality of illumination sources comprising at least one ultraviolet (UV) illumination source. The sensing device further includes at least two cameras arranged in a stereo pair. The sensing device additionally includes a camera with a UV filter, wherein the UV filter is configured to allow wavelengths corresponding to UV light and to block wavelengths corresponding to visible and near infrared light, wherein the UV filter allows transmission of light within an angular range such that the UV filter allows for the transmission of light at one end of the angular range to be equivalent to the transmission of light at an opposite end of the angular range.
A robotic device includes one or more robotic fingers and an attachable wiping tool. The attachable wiping tool includes a wiping component, a container configured to dispense a fluid, and an attachment component coupled to the robotic device. The attachment component is configured to align the one or more robotic fingers with the container such that the one or more robotic fingers, when actuated, engage the container to cause the container to dispense the fluid to the wiping component.
Embodiments of a system and method for generating integrated circuit layouts are described herein. A computer implemented method for generating integrated circuit layouts includes receiving a first layout for an integrated circuit, segmenting the first layout into a plurality of different patches, each patch of the plurality of patches describing a discrete portion of the first layout, identifying a non-compliant patch of the plurality of patches, the non-compliant patch violating a design rule governing the manufacture of the integrated circuit, generating a transformation of the non-compliant patch using a machine learning model, and generating a second layout using the transformation and the first layout, where the second layout is compliant with the design rule.
G06F 30/392 - Floor-planning or layout, e.g. partitioning or placement
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
An electronic device includes a microelectromechanical system (MEMS) rectifier. The MEMS rectifier (100) includes a mainboard (102) and a sub-board (112). The mainboard has one or more radiofrequency (RF) inputs (104) configured to receive an RF signal, and a first electrical contact (106). The sub-board (112) is positioned parallel to the mainboard (102) with a gap in-between, and has a thin film piezoelectric, layer (114), a second electrical (116) contact positioned opposite the first electrical contact (106), and a ground plane (118). The sub-board (112) is configured to vibrate as the RF signal is received at the one or more RF inputs (104), and the thin film piezoelectric layer (114) is configured to generate energy due to the vibration and piezoelectric properties of the thin film piezoelectric layer (114).
An energy harvesting tape (400) comprises a plurality of flexible layers. The plurality of flexible layers includes a solar cell layer (420) configured to capture solar energy, a thermoelectric layer (428) configured to capture thermal energy, one or more piezoelectric layers (424) configured to capture mechanical energy, and an electrode layer (424) configured to capture radiofrequency energy and to transmit a radiofrequency signal. The energy harvesting tape also includes one or more processing units (432) on at least one of the plurality of flexible layers. The one or more processing units are configured to use the captured energy from the plurality of flexible layers to transmit the radiofrequency signal. The energy' harvesting tape has a length, a width, and a thickness, where the length is greater than the width, and the width is greater than the thickness.
H02N 2/18 - Electric machines in general using piezoelectric effect, electrostriction or magnetostriction producing electrical output from mechanical input, e.g. generators
H01L 41/083 - Piezo-electric or electrostrictive elements having a stacked or multilayer structure
H02J 50/00 - Circuit arrangements or systems for wireless supply or distribution of electric power
A method of operating a communication network includes training a neural network and implementing the neural network to determine link availability'. Training the neural network includes receiving first images and signal visibility data for first locations for one or more first nodes in the communication network (406), generating training data based on the first images and the signal visibility data (410), and training the neural network using the training data to output an attenuation category related to attenuation rate of a link based on a training image and a timestamp for the training image (412). Implementing the neural network includes receiving second images for second locations for one or more second nodes in the communication network, determining link availability based on the second images and outputs from the neural network (416), and operating the communication network based on the link availability (418).
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic generation of a supply chain simulation. The methods, systems, and apparatus include actions of obtaining supply chain data of a supply chain, generating a supply chain network graph that represents relationships between locations indicated by the supply chain data, determining classifications of the locations indicated by the supply chain data, determining agent rule models based on the supply chain data, and generating a supply chain simulation based on the supply chain network graph, the classifications of the locations, and the agent rule models.
222 sequestration below a terranean surface; and an above-ground sub-assembly positionable on the terranean surface proximate the underground sub-assembly and including at least one controller communicably coupled to the one or more sensors.
E21B 41/00 - Equipment or details not covered by groups
E21B 47/117 - Detecting leaks, e.g. from tubing, by pressure testing
E21B 47/12 - Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
In one aspect, there is provided a marine sensor platform that includes: a line having a plurality of slots that are spaced apart on the line, a plurality of sensor nodes, each sensor node configured to couple to each of the slots on the line, and each sensor node including at least one sensor, a power source coupled to the plurality of sensor nodes and configured to supply power to the plurality of sensor nodes on the line, and a controller coupled to the power source and the plurality of sensor nodes on the line, wherein the controller is configured to identify a type of the at least one sensor included in each of the sensor nodes when each of the sensor nodes is coupled to the respective slot on the line.
An alert system is configured to track conditions for a tracking system (100). The one or more processors (108) of the alert system are configured to receive data in a payload related to characteristics of tracking devices of the tracking system (402). The one or more processors are then configured to determine a condition timeframe for each condition for a given alert based on the data in the payload (404). The given alert requires at least a first condition and a second condition of the plurality of conditions. The one or more processors are configured to perform a query for historical data for the first condition based on a timestamp of the second condition that is prior to the first condition (406), determine an alert timeframe for the given alert based on the condition timeframes and the historical data (408), and store the given alert and the alert timeframe in a memory of the alert system (410).
Systems and methods for detecting a mechanical disturbance are disclosed. One of the method may comprise the operation steps including: transmitting, by a transmitter (110), a pulse at a preset frequency along a first cable (150); receiving, by a receiver (120), a plurality of signals, wherein each of the plurality of signals travels along the first cable and a second cable (160) connected to the receiver for a corresponding span; calculating one or more differential phases, wherein each differential phase is calculated based on respective phases and the corresponding spans of two of the plurality of signals; and determining a localization of the mechanical disturbance based on the one or more differential phases.
A photonic integrated circuit comprises an optical deinterleaver, including an input region, a dispersive region, and at least two output regions. The input region is adapted to receive an input optical signal including a plurality of channels. The dispersive region is optically coupled to the input region to receive the input optical signal. The dispersive region includes an inhomogeneous arrangement of a first material and a second material to structure the dispersive region to separate the input optical signal into a plurality of multi-channel optical signals, including a first multi-channel optical signal and a second multi-channel optical signal. The at least two output regions, include a first out region and a second output region optically coupled to the dispersive region. The first output region is positioned to receive the first multi-channel optical signal and the second output region is positioned to receive the second multi-channel optical signal.
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
The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind a target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data, identifying, by a first machine-learning model having model parameters learned from the initial sequence data, a first set of aptamer sequences, obtaining, using an in vitro binding selection process, subsequent sequence data including sequences from the first set of aptamer sequences, identifying, by a second machine-learning model having model parameters learned from the subsequent sequence data, a second set of aptamer sequences, determining, using one or more in vitro assays, analytical data for aptamers synthesized from the second set of aptamer sequences, and identifying a final set of aptamer sequences from the second set of aptamer sequences based on the analytical data associated with each aptamer.
A free-space optical communication system includes an optical phased array (OPA) photonic integrated chip (114), a transceiver photonic integrated chip (112), and one or more processors (104). The OPA chip includes a plurality of array elements (120) and a plurality of phase shifters (121). The transceiver chip includes one or more transmitter components (116) and one or more receiver components (118). The one or more processors are configured to transmit a first signal via the OPA chip and the transceiver chip (402), and receive a second signal via the OPA chip and the transceiver chip (410).
The optical tracking module (102, 302) includes an optical phased array (OP A) (112, 312), an analog drive (116, 316), an integrated photodetector (118, 318), and one or more processors (114, 314). The OPA includes a plurality of array elements, and a plurality of phase shifters (113, 313). The analog drive is configured to adjust the plurality of phase shifters. The integrated photodetector is configured to receive light from the OPA. The one or more processors is configured to extract signal information of an incoming beam via the OPA, and control an outgoing beam using the analog drive based on the signal information. The OPA, the analog drive, the integrated photodetector and the one or more processors are in an integrated circuit.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a binding prediction neural network. In one aspect, a method comprises: instantiating a plurality of structure prediction neural networks, wherein each structure prediction neural network has a respective neural network architecture and is configured to process data defining an input polynucleotide to generate data defining a predicted structure of the input polynucleotide; training each of the plurality of structure prediction neural networks; after training the plurality of structure prediction neural networks, determining a respective performance measure of each structure prediction neural network based at least in part on a prediction accuracy of the structure prediction neural network; and generating, based on the performance measures of the structure prediction neural networks, a binding prediction neural network.
An optical phased array (OPA) photonic integrated chip (114) includes a plurality of array elements (120), a plurality of phase shifters (121), a plurality of combiners (230-234), and an edge coupler (236) configured to couple to a single mode waveguide. The plurality of phase shifters includes a layer of phase shifters that has a phase shifter connected to each array element in the plurality of array elements. The plurality of combiners is configured to connect the plurality of phase shifters to the edge coupler. The plurality of combiners includes a first combiner that has a first output that is connected to a second combiner or the edge coupler, and a second output of the first combiner is connected to a photodetector (240). An in-phase light portion at the first combiner is output through the first output, and an out-of-phase light portion at the first combiner is output through the second output.
Methods, systems, and apparatus for generating a recipe for a concrete mixture, comprising: obtaining an optical characterization of a set of particles; determining, based on the optical characterization, physical characteristics of the set of particles; generating a multispherical approximation of the set of particles; selecting, based on the physical characteristics of the set of particles and from a database of performance rules, performance rules applicable to the set of particles; predicting performance of a proposed recipe for a concrete mixture formed from the set of particles by: determining a wet flowability rating of the proposed recipe based on the selected performance rules; and determining a dry packing rating of the proposed recipe based on the multispherical approximation; iteratively altering the proposed recipe and predicting performance of the altered proposed recipe until the predicted performance satisfies performance criteria to obtain a final recipe; and outputting the final recipe.
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
B28C 7/00 - Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
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 processing recycled concrete aggregate (RCA). One of the methods includes obtaining first optical measurements of RCA particles; determining an initial characterization of the RCA particles; iteratively performing a carbonation process on the RCA particles, obtaining second optical measurements of the RCA particles, and determining a second characterization of the RCA particles, where conditions of the carbonation process are initially set based on the initial characterization, and the conditions of the carbonation process are adjusted based on the second characterization; iteratively performing a densification process on the RCA particles, obtaining third optical measurements of the RCA particles, and determining a third characterization of the RCA particles, where conditions of the densification process are initially set based on the initial characterization or the second characterization, and the conditions of the densification process are adjusted based on the third characterization.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for simulating a concrete mixture. One of the methods includes obtaining an optical characterization of physical particles, generating a multispherical approximation of the physical particles, the multispherical approximation having reduced dimensionality compared to the optical characterization, simulating an aggregate mixture by applying the multispherical approximation of the particles to a physics simulator to obtain a predicted performance of the proposed aggregate mixture, selectively altering the aggregate mixture based on a comparison with performance metrics and simulating the altered aggregate mixture until the predicted performance satisfies the performance metrics to obtain a final aggregate mixture, and outputting the final aggregate 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
B28C 7/00 - Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
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
89.
COMPUTER VISION APPROACHES FOR ENVIRONMENTALLY SUSTAINABLE AQUACULTURE
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for environmentally sustainable aquaculture through computer vision for ectoparasite detection and medication dosing. In some implementations, actions include obtaining an image (110) captured by an underwater camera (109); determining one or more fish detections (113a) and one or more ectoparasite detections (113b) based on the image; generating a filtered set (116) of fish detections and ectoparasite detections; providing the filtered set of fish detections and ectoparasite detections to a trained model; and obtaining output of the trained model indicating an intensity (120) of ectoparasite infection.
A method includes determining, for a robotic device that comprises a perception system, a robot planner state representing at least one future path for the robotic device in an environment. The method also includes determining a perception system trajectory by inputting at least the robot planner state into a machine learning model trained based on training data comprising at least a plurality of robot planner states corresponding to a plurality of operator- directed perception system trajectories. The method further includes controlling, by the robotic device, the perception system to move through the determined perception system trajectory.
A latent space is defined to represent sequences using training data and a machine-learning model. The training data identifies sequences of molecules and binding-approximation metrics that characterizes whether the molecules bind to a particular target and/or that approximate an extent to which the molecule is more likely to bind to the particular target than some other molecules. Supplemental training data is accessed that identifies other sequences of other molecules and binding affinity scores quantifying binding strengths between the molecules and the particular target. Projections of representations of the other sequences in the supplemental training data are projected in the latent space using the binding affinity scores. An area or position of interest within the latent space is identified based on the projections. A particular sequence represented within or at the area or position of interest or at the position of interest is identified for downstream processing.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for estimating wave properties of a body of water. A computer-implemented system obtains measurement data for a duration of time from an inertial measurement unit (IMU) onboard an underwater device, generates model input data based on at least the measurement data obtained at the plurality of time points, and processes the model input data to generate model output data indicating one or more wave properties using a machine-learning model. The system further determines, based on at least the one or more wave properties, whether the device is safe to be deployed.
G01C 13/00 - Surveying specially adapted to open water, e.g. sea, lake, river or canal
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
B63B 79/30 - Monitoring properties or operating parameters of vessels in operation for diagnosing, testing or predicting the integrity or performance of vessels
G06N 3/00 - Computing arrangements based on biological models
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium (406), that automate selection of configuration data. One of the methods includes obtaining meal configuration data relating to feeding livestock in an aquaculture environment that includes one or more feeding parameters relating to feeding farmed livestock and one or more criteria. Each of the one or more criteria can be associated with the one or more feeding parameters. Sensor data can be obtained that describes at least one property of the aquaculture environment. A selected set of feeding parameters can be selected using the obtained sensor data and the obtained feeding parameters. The selected set of feeding parameters can be provided to a feeding control subsystem (550).
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 (100), and computer-readable media that implement autonomous seagoing power replenishment watercraft. An example system includes a plurality of marine vessels (110), a plurality of watercraft (120, 130), each watercraft of the plurality of watercraft including a rechargeable electrical power supply and being configured to operate in: a first mode in which the watercraft awaits an assignment to provide electrical energy to a marine vessel of the plurality of marine vessels; a second mode in which the watercraft performs operations including keeping station with an assigned marine vessel and providing electrical energy to the assigned marine vessel from the power supply; and a third mode in which the watercraft recharges the power supply from a charging station (150). The system includes a controller configured to perform operations comprising: transmitting, to a first watercraft, an instruction indicating an assignment of the first watercraft to provide electrical energy to a first marine vessel.
B60L 53/50 - Charging stations characterised by energy-storage or power-generation means
B60L 53/66 - Data transfer between charging stations and vehicles
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
96.
SENSOR FUSION ACROSS A WIDE RANGE OF THE ELECTROMAGNETIC SPECTRUM FOR PLASTICS IDENTIFICATION
Methods and systems for using multiple hyperspectral cameras (112a, 112b, 112) sensitive to different wavelengths to predict characteristics of objects for further processing, including recycling, are described. The multiple hyperspectral images (315) can be used to predict higher resolution spectra by using a trained machine learning model (370). The higher resolution spectra may be more easily analyzed to sort plastics into a recyclability category. The hyperspectral images (315) may also be used to identify and analyze dark or black plastics, which are challenging for SWIR, MWIR, and other wavelengths. The machine learning model (370) may also predict the base polymers and contaminants of plastic objects for recycling. The hyperspectral images (315) may be used to predict recyclability and other characteristics (374, 380) using a trained machine learning model (370).
G01N 21/3563 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
G01N 21/359 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
G01J 3/36 - Investigating two or more bands of a spectrum by separate detectors
G01N 23/223 - Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups , or by measuring secondary emission from the material by irradiating the sample with X-rays or gamma-rays and by measuring X-ray fluorescence
G01N 21/3581 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using Terahertz radiation
G01N 21/71 - Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
G01N 21/90 - Investigating the presence of flaws, defects or contamination in a container or its contents
G01N 21/35 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
G01N 21/84 - Systems specially adapted for particular applications
A hydrogen electrolyzer cell includes a shared reservoir, anode and cathode chambers, and a dividing wall. The shared reservoir holds an electrolytic solution. The anode chamber extends up from the shared reservoir and includes an anode electrode for producing oxygen gas during an electrolysis of the electrolytic solution. An oxygen degassing region is integrated into the anode chamber above the anode electrode. The cathode chamber extends up from the shared reservoir and includes a cathode electrode for producing hydrogen gas during the electrolysis. A hydrogen degassing region is integrated into the cathode chamber above the cathode electrode. The dividing wall extends up from the shared reservoir and separates the anode chamber from the cathode chamber. The dividing wall blocks transport of charged ions within the electrolytic solution across the dividing wall and blocks mixing of the hydrogen and oxygen gases released during the electrolysis.
Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a machine-learning model for predicting event tags. The system obtains asset data for an electric power distribution system in a geographic area. The asset data includes: for each of a plurality of electrical assets of the electrical power distribution system, data indicating one or more characteristics of the electrical asset. The system further obtains sensor data for the electric power distribution system. The sensor data includes measurement data from a plurality of electric sensors. The system generates, by processing the asset data and the sensor data, partition data that includes, for each of the plurality of electrical assets, an assignment that assigns the electrical asset to one of a set of feeder networks.
H02J 3/38 - Arrangements for parallelly feeding a single network by two or more generators, converters or transformers
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
Methods, systems, and computer-readable media that implement a mobile filtration system that provides sustainable, on-demand water filtration while supporting the growth and maintenance of organisms. The method includes determining an environmental parameter associated with a volume of water, determining, based on the determined environmental parameter, a control parameter for an autonomous submersible structure that includes a platform on which marine life grows, and generating, based on determining the control parameter, an instruction for the autonomous submersible structure.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that generate from a first pair and a second pair of images of livestock that are within an enclosure and that are taken at different times using a stereoscopic camera, at least two distance distributions of the aquatic livestock within the enclosure. The distance distributions can be used to determine a measure associated with an optical property of the water within the enclosure. A signal associated with the measure can be provided.
patents
