Abstract: An agent interfaces with a sending computing device and a receiving computing device to automatically adjust one-way or two-way real-time audio and real-time video transmission parameters responsive to changing network conditions and/or application requirements. The agent incorporates a reinforcement learning model that adjusts transmission parameters to maximize an expected value of a sum of future rewards; the expected value of the sum of future rewards is based on a current state of the sending computing, a current action (e.g. a current set of transmission parameters) at the sending computing device and a reward provided by the receiving computing device. The reward is representative of a user-perceived quality of experience at the receiving computing device.

Abstract: An agent interfaces with a sending computing device and a receiving computing device to automatically adjust one-way or two-way real-time audio and real-time video transmission parameters responsive to changing network conditions and/or application requirements. The agent incorporates a reinforcement learning model that adjusts transmission parameters to maximize an expected value of a sum of future rewards; the expected value of the sum of future rewards is based on a current state of the sending computing, a current action (e.g. a current set of transmission parameters) at the sending computing device and a reward provided by the receiving computing device. The reward is representative of a user-perceived quality of experience at the receiving computing device.

Abstract: An agent interfaces with a sending computing device and a receiving computing device to automatically adjust one-way or two-way real-time audio and real-time video transmission parameters responsive to changing network conditions and/or application requirements. The agent incorporates a reinforcement learning model that adjusts transmission parameters to maximize an expected value of a sum of future rewards; the expected value of the sum of future rewards is based on a current state of the sending computing, a current action (e.g. a current set of transmission parameters) at the sending computing device and a reward provided by the receiving computing device. The reward is representative of a user-perceived quality of experience at the receiving computing device.

Abstract: The operation of a wireless power transmission system is to be stabilized. A wireless power supply unit includes a power transmitting module and a power receiving module. The power transmitting module includes a transmission coil to send out AC power. The power receiving module includes: a reception coil to receive from the transmission coil at least a portion of the AC power; and a compensation circuit connected to the reception coil, the compensation circuit including at least one compensation element to counteract at least a part of a leakage reactance or an excitation reactance of a coil pair including the transmission coil and the reception coil.

Abstract: An agent interfaces with a sending computing device and a receiving computing device to automatically adjust one-way or two-way real-time audio and real-time video transmission parameters responsive to changing network conditions and/or application requirements. The agent incorporates a reinforcement learning model that adjusts transmission parameters to maximize an expected value of a sum of future rewards; the expected value of the sum of future rewards is based on a current state of the sending computing, a current action (e.g. a current set of transmission parameters) at the sending computing device and a reward provided by the receiving computing device. The reward is representative of a user-perceived quality of experience at the receiving computing device.

Abstract: Systems and methods for synthesizing high resolution images using image deconvolution and depth information in accordance embodiments of the invention are disclosed. In one embodiment, an array camera includes a processor and a memory, wherein an image deconvolution application configures the processor to obtain light field image data, determine motion data based on metadata contained in the light field image data, generate a depth-dependent point spread function based on the synthesized high resolution image, the depth map, and the motion data, measure the quality of the synthesized high resolution image based on the generated depth-dependent point spread function, and when the measured quality of the synthesized high resolution image is within a quality threshold, incorporate the synthesized high resolution image into the light field image data.

Abstract: Systems and methods for synthesizing high resolution images using image deconvolution and depth information in accordance embodiments of the invention are disclosed. In one embodiment, an array camera includes a processor and a memory, wherein an image deconvolution application configures the processor to obtain light field image data, determine motion data based on metadata contained in the light field image data, generate a depth-dependent point spread function based on the synthesized high resolution image, the depth map, and the motion data, measure the quality of the synthesized high resolution image based on the generated depth-dependent point spread function, and when the measured quality of the synthesized high resolution image is within a quality threshold, incorporate the synthesized high resolution image into the light field image data.

Abstract: Systems and methods for synthesizing high resolution images using image deconvolution and depth information in accordance embodiments of the invention are disclosed. In one embodiment, an array camera includes a processor and a memory, wherein an image deconvolution application configures the processor to obtain light field image data, determine motion data based on metadata contained in the light field image data, generate a depth-dependent point spread function based on the synthesized high resolution image, the depth map, and the motion data, measure the quality of the synthesized high resolution image based on the generated depth-dependent point spread function, and when the measured quality of the synthesized high resolution image is within a quality threshold, incorporate the synthesized high resolution image into the light field image data.

Abstract: Systems and methods for synthesizing high resolution images using image deconvolution and depth information in accordance embodiments of the invention are disclosed. In one embodiment, an array camera includes a processor and a memory, wherein an image deconvolution application configures the processor to obtain light field image data, determine motion data based on metadata contained in the light field image data, generate a depth-dependent point spread function based on the synthesized high resolution image, the depth map, and the motion data, measure the quality of the synthesized high resolution image based on the generated depth-dependent point spread function, and when the measured quality of the synthesized high resolution image is within a quality threshold, incorporate the synthesized high resolution image into the light field image data.

Abstract: Systems and methods for synthesizing high resolution images using image deconvolution and depth information in accordance embodiments of the invention are disclosed. In one embodiment, an array camera includes a processor and a memory, wherein an image deconvolution application configures the processor to obtain light field image data, determine motion data based on metadata contained in the light field image data, generate a depth-dependent point spread function based on the synthesized high resolution image, the depth map, and the motion data, measure the quality of the synthesized high resolution image based on the generated depth-dependent point spread function, and when the measured quality of the synthesized high resolution image is within a quality threshold, incorporate the synthesized high resolution image into the light field image data.

Abstract: A pair of spectacles that can automatically change its power so that a fixation region of interest (ROI) of the user is always in focus. The automatic accommodative spectacle device includes focusing elements, scene sensors, scene analyzer, focus engine, focusing element controller, and power supply. The scene analyzer determines the fixation ROI in the scene by analyzing the scene images. The fixation ROI is used to determine powers for the focusing elements in order to bring the fixation ROI into focus. The focusing element controller carries out the needed optical power adjustment to apply to the focusing elements. Optional light sources may be provided to the scene sensors. Additional optional eye sensor(s), eye light source and line of sight detector can be used to help the scene analyzer extract the fixation ROI.

Abstract: A pair of spectacles that can automatically change its power so that a fixation region of interest (ROI) of the user is always in focus. The automatic accommodative spectacle device includes focusing elements, sensors, line of sight detector, focus engine, focusing element controller, and power supply. The line of sight detector determines the line of sight for the left and right eyes of the user using data from the sensors. The focus engine uses the lines of sight for left and right eyes to determine the user's fixation ROI. The fixation ROI is used to determine powers for the focusing elements in order to bring the fixation ROI into focus. The focusing element controller carries out the needed optical power adjustment to apply to the focusing elements. Optional light sources may be provided.

Abstract: A pair of spectacles that can automatically change its power so that a fixation region of interest (ROI) of the user is always in focus. The automatic accommodative spectacle device includes focusing elements, scene sensors, scene analyzer, focus engine, focusing element controller, and power supply. The scene analyzer determines the fixation ROI in the scene by analyzing the scene images. The fixation ROI is used to determine powers for the focusing elements in order to bring the fixation ROI into focus. The focusing element controller carries out the needed optical power adjustment to apply to the focusing elements. Optional light sources may be provided to the scene sensors. Additional optional eye sensor(s), eye light source and line of sight detector can be used to help the scene analyzer extract the fixation ROI.

Abstract: A pair of spectacles that can automatically change its power so that a fixation region of interest (ROI) of the user is always in focus. The automatic accommodative spectacle device includes focusing elements, sensors, line of sight detector, focus engine, focusing element controller, and power supply. The line of sight detector determines the line of sight for the left and right eyes of the user using data from the sensors. The focus engine uses the lines of sight for left and right eyes to determine the user's fixation ROI. The fixation ROI is used to determine powers for the focusing elements in order to bring the fixation ROI into focus. The focusing element controller carries out the needed optical power adjustment to apply to the focusing elements. Optional light sources may be provided.