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: Hearing protection and communication apparatus using vibration sensors are disclosed. An example wearable electronic device includes means for transducing vibrations associated with speech into a first signal; means for transducing sound associated with ambient noise into a second signal; and means for processing to cause a speaker to output a signal to reduce the ambient noise; detect an identifier in the speech; and cause audio data representative of the speech to be transmitted to a second device associated with the identifier.

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: Methods, apparatus, systems, and articles of manufacture are disclosed to identify and interpret code. An example apparatus includes a natural language (NL) processor to process NL features to identify a keyword, an entity, and an intent of an NL string included in an input retrieved from a user; a database driver to transmit a query to a database including an ontological representation of a version control system, wherein the query is a parameterized semantic query including the keyword, the entity, and the intent of the NL string; and an application programming interface (API) to present to the user a code snippet determined based on the query, the code snippet being at least one of uncommented or non-self-documented.

Abstract: An example apparatus for hearing protection and communication includes safety glasses including a vibration sensor to capture speech from a user. The apparatus also includes hearing protectors communicatively coupled to the safety glasses and one or more other devices. The hearing protectors are to reduce a volume of an ambient sound and play back captured speech from the one or more other devices. The apparatus also further includes a number of wireless communication elements to communicatively couple the safety glasses, the hearing protectors, and at least a second apparatus for hearing protection and communication.

Abstract: An apparatus to facilitate unsupervised incremental clustering learning for multiple modalities is disclosed. The apparatus includes one or more processors to perform first unsupervised clustering on a first input descriptor vector corresponding to a first modality, the first unsupervised clustering to associate the first input descriptor vector with a first identifier; perform second unsupervised clustering on a second input descriptor vector corresponding to a second modality, the second unsupervised clustering to associate the second input descriptor vector with a second identifier; and compare the first identifier of the first unsupervised clustering and the second identifier of the second unsupervised clustering to determine labeling for the first input descriptor vector and the second input descriptor vector.

Abstract: Methods and apparatus for preventing collisions between drones based on drone-to-drone acoustic communications are disclosed. A control system of a first drone includes a route manager to command the first drone to track a first route. The control system further includes a plurality of acoustic sensors arranged on the first drone to detect acoustic signals. The control system further includes a collision prevention engine to determine a second route based on acoustic signals detected by the acoustic sensors, the acoustic signals being received from a second drone while the first drone is tracking the first route. The second route differs from the first route to prevent a drone-to-drone collision between the first drone and the second drone. The collision prevention engine is to cause the route manager to command the first drone to track the second route instead of the first route.

Abstract: An apparatus to facilitate fractional convolutional kernels is disclosed. The apparatus includes one or more processors comprising a convolution circuit of a neural network, the convolution circuit to initialize a set of parameters of a fractional convolutional kernel, the set of parameters comprising at least a fractional derivative parameter that is initialized with a fractional value, and apply the fractional convolutional kernel to input data to convolve the input data to obtain output 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: 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: 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: 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 system is described herein. The system includes at least one hardware processor that is configured to identify a pre-determined acoustic barrier filter, wherein the acoustic barrier filter coincides with the physical acoustic barrier and receive an audio signal within a time window at the first microphone and the second microphone. The hardware processor is also configured to calculate a first measure of variability, a second measure of variability, a third measure of variability, and a fourth measure of variability. The hardware processor further concatenates the first measure of variability, the second measure of variability, the third measure of variability, and the fourth measure of variability to form a feature vector, and inputs the feature vector into a location classifier to obtain an audio source location.

Abstract: An embodiment of a semiconductor package apparatus may include technology to acquire vibration information corresponding to a speaker, and identify the speaker based on the vibration information. Other embodiments are disclosed and claimed.

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: An example apparatus for detecting speech includes an image receiver to receive depth information corresponding to a face. The apparatus also includes a landmark detector to detect the face comprising lips and track a plurality of descriptor points comprising lip descriptor points located around the lips. The apparatus further includes a descriptor computer to calculate a plurality of descriptor features based on the tracked descriptor points. The apparatus includes a pattern generator to generate a visual pattern of the descriptor features over time. The apparatus also further includes a speech recognition engine to detect speech based on the generated visual pattern.