Abstract: Systems, methods, and devices detect wake signals included in audio signals. Methods include receiving a dataset including raw audio data, the raw audio data comprising a plurality of audio samples and associated metadata, and generating, using one or more processing elements, an augmented dataset based on the raw audio data, the augmented dataset comprising a plurality of annotations identifying types of raw audio data. Methods further include generating, using the one or more processing elements, a feature dataset by extracting features from the augmented dataset based, at least in part, on the plurality of annotations, and generating, using the one or more processing elements, a wake signal detection model based, at least in part, on the feature dataset, the wake signal detection model being a machine learning model trained based on the feature dataset.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a method comprises receiving, by a device, incoming motion data from a motion sensor, generating, by the device, an incoming embedding vector based on the incoming motion data, generating, by the device, a predicted embedding vector based on the incoming embedding vector, assigning, by the device, an activity classification based on the predicted embedding vector, and modifying an operating parameter of the device based on the activity classification.

Abstract: The present disclosure provides an approach of receiving, from an entity in a computer network, a wireless data stream including channel state information (CSI). The approach performs a tokenization process on the CSI to generate input embeddings associated with a task. The tokenization process works independently of hardware configurations, parameter configurations, or wireless communication standards of the entity The approach trains a foundational model based on the input embeddings, wherein the foundational model is trained for sensing the task. The approach generates an activity prediction associated with the task.

Abstract: Implementations disclosed describe methods, devices, and systems to perform out-of-distribution and recognition of activities using an inertial measurement unit (IMU) sensor. A method may include receiving motion data by a device from a motion sensor. The method further includes generating image data comprising one or more images based on the motion data. The method further includes determining that a first portion of the image data corresponds to activities outside a classification distribution. The method further includes filtering the image data by removing the first portion from the image and generating filtered image data. The method further includes determining an activity classification, within the classification distribution, based on the filtered image data. The method further includes modifying an operating parameter of the device based on the activity classification.

Abstract: Methods and systems for communication and sensing systems. The disclosed method includes, among other things, responsive to receiving a plurality of channel frequency response (CFR) measurements from an antenna operating at a low-bandwidth range, generating a channel state information (CSI) across a high-bandwidth range based on the plurality of CFR measurements and determining, based on the CSI, a Doppler shift across the high-bandwidth range.

Abstract: The present disclosure provides an approach that captures one or more wireless signals in a geographic area. Each one of the one or more wireless signals includes channel state information (CSI) data. The present disclosure produces a channel state information (CSI) representation based on the CSI data that indicates multiple channel responses corresponding to the one or more wireless signals. The present disclosure filters the CSI representation to remove at least one of the channel responses that correspond to a stationary object within the geographic area to produce a filtered CSI representation. The present disclosure predicts a presence of a moving object within the geographic area based on the filtered CSI representation.

Abstract: Implementations disclosed include methods and systems that include obtaining, by a processing device, a first output from a machine learning model, wherein the first output comprises one or more data samples; identifying a first set of data samples of the one or more data samples that satisfies a threshold criterion; generating, using an explainability tool, a weighted value for each data sample of the first set of data samples; and modifying the machine learning model based at least in part on the weighted value for each data sample of the first set of data samples.

Abstract: A system includes a memory and a processing device, operatively coupled to the memory, to receive, from a client device via a user interface, input data comprising an initial version of a machine learning model, initialize an operating mode of the user interface for machine learning model building, and generate an enhanced version of the machine learning model in accordance with the operating mode.

Abstract: Systems, methods, and devices detect wake signals included in audio signals. Methods include receiving a dataset including raw audio data, the raw audio data comprising a plurality of audio samples and associated metadata, and generating, using one or more processing elements, an augmented dataset based on the raw audio data, the augmented dataset comprising a plurality of annotations identifying types of raw audio data. Methods further include generating, using the one or more processing elements, a feature dataset by extracting features from the augmented dataset based, at least in part, on the plurality of annotations, and generating, using the one or more processing elements, a wake signal detection model based, at least in part, on the feature dataset, the wake signal detection model being a machine learning model trained based on the feature dataset.

Abstract: A system includes memory and a processing device, operatively coupled to the memory, to obtain an input signal corresponding to data obtained from a data source, extract a set of features using the input signal, generate a set of feature tracking data from the set of features, compress a machine learning model to obtain a compressed model by identifying a subset of features based on the set of tracking data, and use the compressed model to make a prediction based on the set of feature tracking data. The set of features includes a set of confidence features and a set of uncertainty features, and the set of feature tracking data includes a set of confidence feature tracking data and a set of uncertainty feature tracking data.

Abstract: Implementations disclosed describe methods, devices, and systems to perform out-of-distribution and recognition of activities using an inertial measurement unit (IMU) sensor. A method may include receiving motion data by a device from a motion sensor. The method further includes generating image data comprising one or more images based on the motion data. The method further includes determining that a first portion of the image data corresponds to activities outside a classification distribution. The method further includes filtering the image data by removing the first portion from the image and generating filtered image data. The method further includes determining an activity classification, within the classification distribution, based on the filtered image data. The method further includes modifying an operating parameter of the device based on the activity classification.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a method comprises receiving, by a device, incoming motion data from a motion sensor, generating, by the device, an incoming embedding vector based on the incoming motion data, generating, by the device, a predicted embedding vector based on the incoming embedding vector, assigning, by the device, an activity classification based on the predicted embedding vector, and modifying an operating parameter of the device based on the activity classification.