Abstract: The various implementations described herein include a video camera assembly that includes: (1) a housing; (2) an image sensor positioned within the housing and having a field of view corresponding to a scene in the smart home environment; and (3) a concave-shaped front face positioned in front of the image sensor such that light from the scene passes through the front face prior to entering the image sensor; where the front face includes: (a) an inner section corresponding to the image sensor; and (b) an outer section between the housing and the inner section, the outer section having a concave shape that extends from an outer periphery of the outer section to an inner periphery of the outer section; and where the concave shape extends around an entirety of the outer periphery.

Abstract: Disclosed are compounds having the formula: wherein q, r, s, A, B, C, RA1, RA2, RB1, RB2, RC1, RC2, R3, R4, R5, R6, R14, R15, R16, and R17, are as defined herein, or a tautomer thereof, or a salt, particularly a pharmaceutically acceptable salt, thereof.

Abstract: The various implementations described herein include a video camera assembly that includes: (1) a housing; (2) an image sensor encased in the housing and configured to capture activity of the smart home environment; (3) a wireless radio configured to transmit video frames captured by the image sensor to an electronic device via a remote server; (4) at least one infrared transmitter configured to selectively illuminate the smart home environment; (5) one or more circuit boards encased in the housing, the one or more circuit boards including at least one processor mounted thereon; and (6) a heating component coupled to the image sensor, the heating component configured to continuously maintain the image sensor at a temperature above a threshold temperature while the image sensor is capturing the activity of the smart home environment.

Abstract: The various implementations described herein include a video camera assembly that includes: (1) a housing; (2) an image sensor positioned within the housing and having a field of view corresponding to a scene in the smart home environment; and (3) a concave-shaped front face positioned in front of the image sensor such that light from the scene passes through the front face prior to entering the image sensor; where the front face includes: (a) an inner section corresponding to the image sensor; and (b) an outer section between the housing and the inner section, the outer section having a concave shape that extends from an outer periphery of the outer section to an inner periphery of the outer section; and where the concave shape extends around an entirety of the outer periphery.

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.