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: Methods, apparatus, systems, and articles of manufacture to detect the location of sound sources external to computing devices are disclosed. An apparatus, to determine a direction of a source of a sound relative to a computing device, includes a cross-correlation analyzer to generate a vector of values corresponding to a cross-correlation of first and second audio signals corresponding to the sound. The first audio signal is received from a first microphone of the computing device. The second audio signal is received from a second microphone of the computing device. The apparatus also includes a location analyzer to use a machine learning model and a set of the values of the vector to determine the direction of the source of the sound.

Abstract: Systems, apparatuses and methods may provide for origination camera technology that extracts data from multimodal sensor signals associated with a scene and generates a five-dimensional (5D) representation of the scene, wherein the 5D representation includes a three-dimensional (3D) visual representation, a one-dimensional (1D) temporal representation, and a 1D branch representation. The technology may also store the 5D representation as a set of abstract descriptors.

Abstract: Methods and apparatus for drone collision avoidance. Processing circuitry of a drone extracts information from an encoded image captured by a detection device with a field view overlapping the encoded image. Based on the extracted information a determination is made whether a collision will occur on the flight trajectory of the drone with an external source. The flight trajectory of the drone is then altered to avoid a collision.

Abstract: In one example a management system for an autonomous vehicle, comprises a first image sensor to collect first image data in a first geographic region proximate the autonomous vehicle and a second image sensor to collect second image data in a second geographic region proximate the first geographic region and a controller communicatively coupled to the first image sensor and the second image sensor and comprising processing circuitry to collect the first image data from the first image sensor and second image data from the second image sensor, generate a first reliability index for the first image sensor and a second reliability index for the second image sensor, and determine a correlation between the first image data and the second image data. Other examples may be described.

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: 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: Methods and apparatus are described for drone collision avoidance that includes extracting first feature information from an image. The image is captured from a first camera oriented in a direction. Second feature information is received from an external source. The second feature information is extracted from a second image of the environment captured by a second camera oriented in the direction. The first feature information and the second feature information are matched. A second local frame of reference of the second feature information is transformed to a first local frame of reference of the first feature information to determine a location of the external source. If a collision with the external source will occur is determined based on the location of the external source and a current flight trajectory.

Abstract: Systems and methods for multi-modal user device authentication are disclosed. An example electronic device includes a first sensor, a microphone, a first camera, and a confidence analyzer to authenticate a subject as the authorized user in response to a user presence detection analyzer detecting a presence of the subject and one or more of (a) an audio data analyzer detecting a voice of an authorized user or (b) an image data analyzer detecting a feature of the authorized user. The example electronic device includes a processor to cause the electronic device to move from a first power state to a second power state in response to the confidence analyzer authenticating the user as the authorized user. The electronic device is to consume a greater amount of power in the second power state than the first power state.

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 and apparatus are described for drone collision avoidance that includes extracting first feature information from an image. The image is captured from a first camera oriented in a direction. Second feature information is received from an external source. The second feature information is extracted from a second image of the environment captured by a second camera oriented in the direction. The first feature information and the second feature information are matched. A second local frame of reference of the second feature information is transformed to a first local frame of reference of the first feature information to determine a location of the external source. If a collision with the external source will occur is determined based on the location of the external source and a current flight trajectory.

Abstract: In one example a management system for an autonomous vehicle, comprises a first image sensor to collect first image data in a first geographic region proximate the autonomous vehicle and a second image sensor to collect second image data in a second geographic region proximate the first geographic region and a controller communicatively coupled to the first image sensor and the second image sensor and comprising processing circuitry to collect the first image data from the first image sensor and second image data from the second image sensor, generate a first reliability index for the first image sensor and a second reliability index for the second image sensor, and determine a correlation between the first image data and the second image data. Other examples may be described.

Abstract: Methods and apparatus for drone collision avoidance. Processing circuitry of a drone extracts information from an encoded image captured by a detection device with a field view overlapping the encoded image. Based on the extracted information a determination is made whether a collision will occur on the flight trajectory of the drone with an external source. The flight trajectory of the drone is then altered to avoid a collision.