Abstract: A component of a robotic system, including: processor circuitry; and a non-transitory computer-readable storage medium including instructions that, when executed by the processor circuitry, cause the processor circuitry to: train a neuro-capability map plugin, which is a continuous or semi-continuous resolution neural network component encoded with kinematic capability attributes with respect to an action to be performed by a robot in a workspace; and publish the neuro-capability map plugin to a robotic skills repository where it is obtainable by robotic controller circuitry for embedding within a neural network usable perform one or more inferences to control the robot to perform the action.

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 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: 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: 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: System and techniques for tool position determination in a robotic appendage are described herein. A robotic appendage is put through a rotational movement to induce acceleration in a tool mounted to the appendage. A model for acceleration is created from positional kinematics of the appendage. A measurement of acceleration is taken at the tool and fit to the model to determine distance from the axis of rotation to the tool. The distance is provided for use in control or modeling of the robotic appendage.

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: 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, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes at least one memory; instructions; and processor circuitry to execute the instructions. The processor circuitry executes the instructions to extract static and dynamic data from a packet associated with a request for service by an edge device, the static data to change less frequently than the dynamic data. The processor circuitry executes the instructions to generate a first plurality of probability distributions using the static data. The processor circuitry executes the instructions to generate a second plurality of probability distributions using the dynamic data. The processor circuitry executes the instructions to calculate a confidence value for a first helper compute unit of a plurality of helper compute units, the confidence value. The processor circuitry executes the instructions to assign the first helper compute unit the request for service based on the confidence value.

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, apparatus, systems, and articles of manufacture are disclosed to enhance and audio signal.

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: 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: Various systems and methods for implementing an enhanced occupant collision safety system in a vehicle are described herein. A system for controlling deployment of vehicle safety mechanisms of a host vehicle, includes a sensor array interface to receive acceleration data from an impact sensor; an image processor to receive events from an event camera, the event camera configured to monitor a scene of an interior of the host vehicle that includes an occupant; and an occupant safety system controller to: cluster the events to discriminate between events associated with a background of the scene and events associated with a body part of the occupant; detect a collision based on the acceleration data; verify the collision based on the events associated with the background of the scene; and in response to verifying the collision, cause a collision safety mechanism of the host vehicle to deploy.

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: Various aspects of methods, systems, and use cases include a robotic system and control or use of the robotic system. A method for autonomous movement of the robotic system may include receiving a target location, identifying robotic components of the robotic system that are in contact with a surface, and determining a robotic component of the robotic components to activate to cause the robotic system to move closer to the target location. The method may include activating, based on the determination, a motor of the robotic component to push against the surface to cause the robotic system to move towards the target location.

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: Techniques are disclosed for the acceleration of the operation of a probabilistic sampling device for applications including homography estimation and wireless signal detection, which may include reducing the number of iterations associated with probabilistic sampling. Techniques are also disclosed for corner feature extraction from event-based cameras, which may be implemented via an Advanced Driver Assistance System (ADAS) or Autonomous Driving (AD) system.

Abstract: Disclosed herein are systems and methods for privacy-preserving facemask-compliance-level measurement. In an embodiment, a mask-compliance measurement system includes a processor that is configured to generate, from an image of a person, a facial depth image of a region of a face of the person, and to generate facial wavelet descriptors from the facial depth image. The processor is also configured to determine spectral-density values of the wavelet descriptors, and to analyze the spectral-density values to generate a mask-wearing-compliance result for the person. In an embodiment, the analyzing includes using a classification model that is trained to classify sets of spectral-density values with respect to facemask wearing in images from which the spectral-density values were derived.

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.