Abstract: A disclosed example includes providing vibration information to a model, the vibration information corresponding to a first vibration measured at a first mobile device when the first mobile device is in a state of non-use by a user, the model based on a plurality of vibration patterns that correspond to second vibrations measured by second mobile devices in different environments; identifying, using the model, one of the vibration patterns that corresponds to the vibration information; determining an environment of the first mobile device based on the one of the vibration patterns; and instructing the first mobile device to modify a functionality of the first mobile device based on the environment.

Abstract: A system, article, and method of acoustic angle of arrival detection uses both same-time and delayed-time audio signal value comparisons in a time domain that are input to a classifier neural network.

Abstract: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.

Abstract: A unmanned aerial vehicle can include a modem comprising a first antenna port and a second antenna port, an omnidirectional antenna connected to the first antenna port, and antennas configured to generate a directional transmission pattern connected to the second antenna port, the antennas including (a) an array of omni-directional antennas and (b) multiple directional antennas

Abstract: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.

Abstract: A disclosed example includes providing vibration information to a model, the vibration information corresponding to a first vibration measured at a first mobile device when the first mobile device is in a state of non-use by a user, the model based on a plurality of vibration patterns that correspond to second vibrations measured by second mobile devices in different environments; identifying, using the model, one of the vibration patterns that corresponds to the vibration information; determining an environment of the first mobile device based on the one of the vibration patterns; and instructing the first mobile device to modify a functionality of the first mobile device based on the environment.

Abstract: Technology for a mobile device is described. The mobile device can include a vibration motor operable to generate a vibration, and a sensor operable to capture a plurality of vibration signals that result from the vibration. The mobile device can provide the plurality of vibration signals to a model running on the mobile device. The model can include a plurality of predefined patterns that correspond to vibration signals produced by mobile devices residing on different surfaces. The mobile device can identify, using the model, a predefined pattern in the plurality of predefined patterns that substantially corresponds to the plurality of vibration signals based on a confidence level that exceeds a threshold. The mobile device can determine a type of surface on which the mobile device resides based on the predefined pattern identified using the model.

Abstract: A component of an Autonomous Vehicle (AV) system, the component having at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to decode data encoded in a signal, wherein the data identifies a pattern of protrusions embedded in a driving surface, the signal being received from at least one vehicle sensor resulting from a vehicle driving over the pattern of protrusions in the driving surface.

Abstract: Technology for a mobile device is described. The mobile device can include a vibration motor operable to generate a vibration, and a sensor operable to capture a plurality of vibration signals that result from the vibration. The mobile device can provide the plurality of vibration signals to a model running on the mobile device. The model can include a plurality of predefined patterns that correspond to vibration signals produced by mobile devices residing on different surfaces. The mobile device can identify, using the model, a predefined pattern in the plurality of predefined patterns that substantially corresponds to the plurality of vibration signals based on a confidence level that exceeds a threshold. The mobile device can determine a type of surface on which the mobile device resides based on the predefined pattern identified using the model.

Abstract: Techniques are disclosed to use existing vehicle speakers alone or in conjunction with other sensors (e.g. SRS sensors and/or microphones) that may already be implemented as part of the vehicle to identify acoustic signatures. Suitable low-cost and widely available hardware components (e.g., relays) may be used to modify the vehicle's existing speakers for a bi-directional mode of operation. Moreover, the vehicle's existing of audio amplifiers may be used to amplify signals collected by the speakers when operating in “reverse,” and process these collected signals to determine vehicle state information.

Abstract: Methods, systems, and apparatus for audio noise reduction from a drone are disclosed. An example apparatus includes an acoustic sensor to gather acoustic data and at least one rotational motion sensor to gather rotational motion data of a first rotor and second rotational motion data of a second rotor. The example apparatus also includes an analyzer to identify a first filter that matches the first rotational motion data and identify a second filter that matches the second rotational motion data. The analyzer also is to filter the acoustic data into filtered acoustic data with the first identified filter and the second identified filter and generate an audio signal based on the filtered acoustic data.

Abstract: Technology for a mobile device is described. The mobile device can include a vibration motor operable to generate a vibration, and a sensor operable to capture a plurality of vibration signals that result from the vibration. The mobile device can provide the plurality of vibration signals to a model running on the mobile device. The model can include a plurality of predefined patterns that correspond to vibration signals produced by mobile devices residing on different surfaces. The mobile device can identify, using the model, a predefined pattern in the plurality of predefined patterns that substantially corresponds to the plurality of vibration signals based on a confidence level that exceeds a threshold. The mobile device can determine a type of surface on which the mobile device resides based on the predefined pattern identified using the model.

Abstract: Systems, apparatuses and methods may provide for technology that adjusts a plurality of weights in a neural network model and adjusts a plurality of activation functions in the neural network model. The technology may also output the neural network model in response to one or more conditions being satisfied by the plurality of weights and the plurality of activation functions. In one example, two or more of the activation functions are different from one another and the activation functions are adjusted on a per neuron basis.

Abstract: Technology for a mobile device is described. The mobile device can include a vibration motor operable to generate a vibration, and a sensor operable to capture a plurality of vibration signals that result from the vibration. The mobile device can provide the plurality of vibration signals to a model running on the mobile device. The model can include a plurality of predefined patterns that correspond to vibration signals produced by mobile devices residing on different surfaces. The mobile device can identify, using the model, a predefined pattern in the plurality of predefined patterns that substantially corresponds to the plurality of vibration signals based on a confidence level that exceeds a threshold. The mobile device can determine a type of surface on which the mobile device resides based on the predefined pattern identified using the model.

Abstract: Methods, systems, and apparatus for audio noise reduction from a drone are disclosed. An example apparatus includes an acoustic sensor to gather acoustic data and at least one rotational motion sensor to gather rotational motion data of a first rotor and second rotational motion data of a second rotor. The example apparatus also includes an analyzer to identify a first filter that matches the first rotational motion data and identify a second filter that matches the second rotational motion data. The analyzer also is to filter the acoustic data into filtered acoustic data with the first identified filter and the second identified filter and generate an audio signal based on the filtered acoustic data.

Abstract: A system, article, and method of acoustic angle of arrival detection uses both same-time and delayed-time audio signal value comparisons in a time domain that are input to a classifier neural network.

Abstract: Methods, systems, and apparatus for audio noise reduction from a drone are disclosed. An example apparatus includes a first sensor to gather acoustic data and a second sensor to gather rotational motion data of a rotor. The example apparatus also includes an analyzer to match the rotational motion data to a filter and filter the acoustic data using the filter. The analyzer also is to generate an audio signal based on the filtered acoustic data.

Abstract: Methods, systems, and apparatus for audio noise reduction from a drone are disclosed. An example apparatus includes a first sensor to gather acoustic data and a second sensor to gather rotational motion data of a rotor. The example apparatus also includes an analyzer to match the rotational motion data to a filter and filter the acoustic data using the filter. The analyzer also is to generate an audio signal based on the filtered acoustic data.

Abstract: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.