Abstract: A method includes determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes generating feature data based on the waveform data. The method further includes providing the feature data as input to a first machine learning model to predict a future action of a device associated with at least a portion of the electromagnetic waveform and initiating a response action based on the predicted future action.

Abstract: In a particular embodiment, recording data associated with an unmanned aerial vehicle (UAV) is disclosed that includes a blockchain manager receiving a transaction message associated with a UAV. The particular embodiment also includes the blockchain manager using the information within the transaction message to create a block of data. In this particular embodiment, the blockchain manager stores the created block of data in a blockchain data structure associated with the UAV.

Abstract: Flight path deconfliction among unmanned aerial vehicles is disclosed. Telemetry data received from an unmanned aerial vehicle (UAV) and air traffic data received from a server or other data sources are analyzed, using deconfliction circuitry, to determine whether a flight path conflict indicative of a potential collision exists. The deconfliction circuitry reroutes the flight path of the UAV to avoid the flight path conflict, and transmits, in dependence upon the rerouted flight path, navigation instructions to the UAV for avoiding the potential collision. The deconfliction circuitry includes hardware-implemented logic optimized for processing the navigation data.

Abstract: Making a determination regarding consensus using proofs of altitude of unmanned aerial vehicles (UAVs), including: receiving a request for a consensus from a pool of Unmanned Aerial Vehicles (UAVs), wherein membership in the pool of UAVs is based on an altitude threshold; sending, to each UAV in the pool of UAVs, a transaction request associated with the request for the consensus; receiving, from each UAV in the pool of UAVs, a corresponding response of a plurality of responses to the transaction request; and generating, based on the plurality of responses, a determination for the consensus.

Abstract: A method includes obtaining route information at an unmanned aerial vehicle (UAV). The route information indicates a plurality of zones between an origin and a destination and a corresponding set of mobile devices for each zone. The method includes receiving first control data at the UAV from a first mobile device while in a first zone of the plurality of zones. The first mobile device is included in a first set of mobile devices corresponding to the first zone. The method further includes receiving second control data at the UAV from a second mobile device while in a second zone of the plurality of zones. The second mobile device is included in a second set of mobile devices corresponding to the second zone.

Abstract: A method includes determining a trainable model to provide to a trainer, the trainable model determined based on modification of one or more models of a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes providing the trainable model to the trainer. The method further includes adding a trained model, output by the trainer based on the trainable model, as input to a second epoch of the genetic algorithm, the second epoch subsequent to the first epoch.

Abstract: In a particular embodiment, coordinating an aerial search among unmanned aerial vehicles is disclosed that includes receiving, by a server in a UAV transportation ecosystem, search area data, accessing, by the server, UAV parameters for a type of UAV, determining, by the server in dependence upon the search area data and the UAV parameters, a number of UAVs needed to complete a coordinated aerial search of a search area within a time limit, and partitioning, by the server, the search area into a plurality of partitions, wherein the number of partitions is equal to the number of UAVs.

Abstract: In a particular embodiment, blockchain-based hybrid authentication is disclosed that includes receiving, by a smart contract controller, an invocation of a smart contract and accessing, by the smart contract controller, one or more parameters associated with a client account. In this embodiment, the smart contract determines whether the one or more parameters fulfill the smart contract. In response to determining that the one or more parameters fulfill the smart contract, the smart contract provides an attestation of an identity associated with the client account.

Abstract: In a particular embodiment, monotonic partitioning in unmanned aerial vehicle (UAV) search and surveillance is disclosed that includes receiving, by server, a request for search route information, the request including a geographical search area, transforming the geographical search area into a plurality of monotonic partitions, determining, for each monotonic partition, a sweep search pattern for UAV navigation, and generating, in dependence upon the search patterns, search route information for one or more UAVs.

Abstract: In a particular embodiment, methods, systems, apparatuses, and computer program products for utilizing an unmanned aerial vehicle for emergency response are disclosed that include determining, by a UAV, a disinfection area; flying, by the UAV, over the determined disinfection area; and releasing, by the UAV, a disinfectant while flying over the determined disinfection area.

Abstract: Methods and devices for confirmation of successful delivery by an unmanned aerial vehicle (UAV) are disclosed. A UAV is configured to navigate to a delivery destination, authenticate a receiving zone at the delivery destination, release, in dependence upon authentication of the receiving zone, the package into the receiving zone, capture, by a camera of the UAV, an image of the receiving zone, and generate delivery confirmation data including the image the receiving zone. A recipient device is configured to detect, by the device disposed in a receiving zone for receiving a package, a UAV in proximity to a receiving zone, authenticate the UAV, detect delivery of the package in the receiving zone, capture, by a camera of the device, an image of the package, and generate delivery confirmation data including the image the package and location data obtained from a GPS sensor in the device.

Abstract: A method includes determining a trainable model to provide to a trainer, the trainable model determined based on modification of one or more models of a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes providing the trainable model to the trainer. The method further includes adding a trained model, output by the trainer based on the trainable model, as input to a second epoch of the genetic algorithm, the second epoch subsequent to the first epoch.

Abstract: In a particular embodiment, decentralized oracles in an unmanned aerial vehicle (UAV) transportation ecosystem includes: receiving, by an oracle node of a plurality of oracle nodes, from a validating node of a plurality of validating nodes of a UAV transportation ecosystem, a request for data for fulfillment of a smart contract, wherein the plurality of oracle nodes are communicatively coupled via an overlay network implemented using a network communicatively coupling the plurality of validating nodes; generating, based on responses to the request from the plurality of oracle nodes, the data for fulfillment of the smart contract; and providing the data for fulfillment of the smart contract.

Abstract: A method includes selecting a subset of models from a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model. The method includes determining a rate of improvement associated with prior backpropagation iterations. The method includes selecting, based on the rate of improvement, one of the trainable model or a prior trainable model as a selected trainable model. The method includes generating the trained model including training the selected trainable model. The method includes adding the trained model as input to a second epoch of the genetic algorithm that is subsequent to the first epoch.

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Abstract: Logistics and transportation technologies are disclosed. A globally unique identifier (GUID) of a user is used to access a rideshare router that determine which one or multiple modes of transport should be used for various segments of a planned journey. The modes can include both terrestrial and unmanned aerial options. The GUID of the user is used to post information to a distributed ledger, such as a blockchain or hyperledger. A smart key fob of a vehicle provides a user with access to mobility services and a full-on computer experience. Logistics and transportation data, including on-board diagnostics data, can be used to predict vehicle maintenance, calculate a vehicle health score, determine natural language generation of explanations regarding vehicle health, and even calculate a carbon footprint score.

Abstract: Methods, systems, apparatuses, and computer program products for utilizing visualization for managing an unmanned aerial vehicle (UAV) are disclosed. In a particular embodiment, utilizing visualization for managing a UAV includes providing to a user, by a management controller, a visualization that displays an environment and representations of one or more UAVs associated with a user; receiving from the user, by the management controller, data indicating the user applying one or more management controls within the visualization; and in response to receiving the data indicating the user applying the one or more management controls within the visualization, initiating, by the management controller, an event that modifies the one or more UAVs.

Abstract: Uses of artificial intelligence in battery technology including a method that includes receiving a trained model, receiving sensor data from at least one sensor associated with a battery, and executing the trained model by a processor. Executing the trained model includes providing the sensor data as input to the trained model to generate a model output. The method also includes sending, from the processor to a charge controller coupled to the battery, a control signal that is based on the model output and automatically, by the charge controller, initiating or terminating charging of the battery based on the control signal.

Abstract: An architecture for data analysis of geographic data and associated context data. The data analysis of the geographic data and the associated context data includes receiving a query and determining a data model to output information requested by the query. The data analysis of the geographic data and the associated context data also includes accessing the geographic data and the associated context data from a data repository, providing the geographic data and the associated context data as input to the data model, and generating output including the information in response to the query.

Abstract: A device includes communication circuitry configured to receive a message indicating an observation of an agent device. The device further includes a processor coupled to the communication circuitry and a memory. The memory stores instructions that are executable by the processor to cause the processor to perform operations. The operations include accessing a blockchain data structure. The blockchain data structure includes one or more blocks including data descriptive of observations of a plurality of agent devices, where the plurality of agent devices including the agent device. The operations also include determining, based on one or more blocks of the blockchain data structure, a behavior of the agent device. The operations also include determine whether the behavior satisfies a behavior criterion associated with the agent device.