Abstract: The disclosed computer-implemented method may include establishing a virtual boundary for a virtual-world environment in reference to a real-world environment, and determining whether the virtual boundary requires a correction. The method may include providing, in response to determining that the virtual boundary requires the correction, and alert and, in response to the alert, receiving a request from a user to modify the virtual boundary. The method may further include monitoring, in response to the request from the user, an orientation of a direction indicator to generate orientation data, and modifying the virtual boundary based on the orientation data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A system uses a machine learning model trained using multitask learning for processing audio signals of different types. The system may be used in a device such as a headset, smart glasses, or a smart watch, and may be used to make predictions based on different types of audio signals, for example, for classifying acoustic events based on audio signal or for keyword spotting to detect wake words. The use of a single machine learning model for analyzing different types of audio signals improves storage efficiency as well as energy efficiency of devices compared to systems that use a different machine learning model for processing each type of audio signal.

Abstract: The disclosed computer-implemented method may include establishing a virtual boundary for a virtual-world environment in reference to a real-world environment, and determining whether the virtual boundary requires a correction. The method may include providing, in response to determining that the virtual boundary requires the correction, and alert and, in response to the alert, receiving a request from a user to modify the virtual boundary. The method may further include monitoring, in response to the request from the user, an orientation of a direction indicator to generate orientation data, and modifying the virtual boundary based on the orientation data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include establishing a virtual boundary for a virtual-world environment in reference to a real-world environment, and determining whether the virtual boundary requires a correction. The method may include providing, in response to determining that the virtual boundary requires the correction, and alert and, in response to the alert, receiving a request from a user to modify the virtual boundary. The method may further include monitoring, in response to the request from the user, an orientation of a direction indicator to generate orientation data, and modifying the virtual boundary based on the orientation data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include establishing a virtual boundary for a virtual-world environment in reference to a real-world environment, and determining whether the virtual boundary requires a correction. The method may include providing, in response to determining that the virtual boundary requires the correction, and alert and, in response to the alert, receiving a request from a user to modify the virtual boundary. The method may further include monitoring, in response to the request from the user, an orientation of a direction indicator to generate orientation data, and modifying the virtual boundary based on the orientation data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving an indication of a reference elevation representing a plane of a real-world environment and establishing, with respect to the reference elevation, a virtual boundary for a virtual-world environment. The method may include receiving a request from a user to modify the virtual boundary and in response to the request from the user, monitoring an orientation of a direction indicator to generate orientation data. The method may also include modifying the virtual boundary based on the reference elevation and the orientation data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving an indication of a baseline elevation representing a base plane of a real-world environment, receiving a request via a user device to initiate a boundary definition state, monitoring an elevation and an orientation of the user device during manipulation to generate elevation data and orientation data during the boundary definition state, and defining a virtual boundary in a virtual-world environment based on the baseline elevation, the elevation data, and the orientation data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving an indication of a reference elevation representing a plane of a real-world environment and establishing, with respect to the reference elevation, a virtual boundary for a virtual-world environment. The method may include receiving a request from a user to modify the virtual boundary and in response to the request from the user, monitoring an orientation of a direction indicator to generate orientation data. The method may also include modifying the virtual boundary based on the reference elevation and the orientation data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Image classification system contains a CNN based IC configured for extracting features out of input data by performing convolution operations using filter coefficients of ordered convolutional layers and a classifier IC configured for classifying the input data using reduced set of the extracted features based on a light-weight classifier. Light-weight classifier is derived by: training filter coefficients of the ordered convolutional layers using a dataset containing N labeled data, the trained filter coefficients are for the CNN based IC; outputting respective extracted features of the N labeled data after performing convolution operations of ordered convolutional layers using the trained filter coefficients, each labeled data contains X features; creating the reduced set of the extracted features by eliminating those of the X features that contain zeros in at least M of the N labeled data; and adjusting M until the light-weight classifier achieves satisfactory results using the reduced set.

Abstract: Apparatus for recognition of handwritten Chinese characters contains a bus, an input means connecting to the bus for receiving input imagery data created from a handwritten Chinese character, a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit operatively connecting to the bus for extracting features out of the input imagery data using pre-trained filter coefficients of a plurality of order convolutional layers stored therein, a memory connecting the bus, the memory being configured for storing weight coefficients of fully-connected (FC) layers, a processing unit connecting to the bus for performing computations of FC layers to classify the extracted features from the CNN based integrated circuit to a particular Chinese character in a predefined Chinese character set, and a display unit connecting to the bus for displaying the particular Chinese character.

Abstract: Image classification system contains a CNN based IC configured for extracting features out of input data by performing convolution operations using filter coefficients of ordered convolutional layers and a classifier IC configured for classifying the input data using reduced set of the extracted features based on a light-weight classifier. Light-weight classifier is derived by: training filter coefficients of the ordered convolutional layers using a dataset containing N labeled data, the trained filter coefficients are for the CNN based IC; outputting respective extracted features of the N labeled data after performing convolution operations of ordered convolutional layers using the trained filter coefficients, each labeled data contains X features; creating the reduced set of the extracted features by eliminating those of the X features that contain zeros in at least M of the N labeled data; and adjusting M until the light-weight classifier achieves satisfactory results using the reduced set.