Abstract: One embodiment of a method for determining video quality includes generating a first comparison video based on a source video, encoding a downscaled and denoised version of the source video to generate an encoded video, decoding the encoded video to generate a decoded video, generating a second comparison video based on the decoded video, and computing a video quality score based on the first comparison video and the second comparison video, where the encoded video is selected for transmission to a client device based on the video quality score.

Abstract: One embodiment of a method for determining video quality includes generating a first comparison video based on a source video, encoding a downscaled and denoised version of the source video to generate an encoded video, decoding the encoded video to generate a decoded video, generating a second comparison video based on the decoded video, and computing a video quality score based on the first comparison video and the second comparison video, where the encoded video is selected for transmission to a client device based on the video quality score.

Abstract: A cockpit display system includes a cockpit, a first display apparatus, a second display apparatus, a first light sensor, a second light sensor and a brightness distribution calculation module. The first light sensor is suitable for detecting a first ambient light brightness. The second light sensor is suitable for detecting a second ambient light brightness. The brightness distribution calculation module is suitable for respectively calculating a first brightness, a second brightness, a third brightness and a fourth brightness of the first display area and the second display area of the first display apparatus and the third display area and the fourth display area of the second display apparatus under a same display gray level according to the first ambient light brightness and the second ambient light brightness. The first brightness, the second brightness, the third brightness and the fourth brightness are different from each other.

Abstract: A motion estimation and motion compensation (MEMC) system with calibration function and a parameter calibration method thereof relate to a parameter calibration method for MEMC. The parameter calibration method is configured to calibrate an MEMC program. The parameter calibration method includes storing a default feature set; performing the MEMC program on a calibrated video to fetch a testing feature set; generating a calibration parameter according to a difference vale between the default feature set and the testing feature set; and adjusting a correction parameter of the MEMC calibration program in accordance with the calibration parameter.

Abstract: Rotary high-voltage and high-current connecting device has a plug and a socket. The socket has a connection unit, nut unit, insulating base, insulating cap, and contact protection cover. The plug has a connection unit, screw-like bolt unit, insulating base, and insulating cap. The nut unit is inserted into a concave structure of the socket insulating base. The socket connection unit is inserted into the insulating base and a copper sleeve is placed on the connection unit and the nut unit. Windows are provided on the insulating cap to lock with a detent structure of the socket insulating base. The contact protection cover is located on top of the nut unit. The insulating cap is joined with the plug insulating base via detent means and windows, while the plug is inserted into the socket by means of the nut unit and a bolt assembly.

Abstract: One embodiment of a method for transmitting film grain parameters to one or more client devices for use when playing back video content includes generating, based on one or more film grain parameters associated with video content, one or more compact representations of the one or more film grain parameters associated with the video content, and transmitting the one or more compact representations to a client application executing on a client device, where the client application adds film grain to the video content, for playback, based on the one or more compact representations.

Abstract: A communication equipment receives a plurality of network sessions and includes a memory and a processor. The memory stores a plurality of software modules. The processor is connected to the memory and configured to implement the following steps. A network session inferring step is performed to execute an inference software module, and the inference software module processes at least one network packet of each of the network sessions and a packet characteristic module according to a machine learning algorithm to infer a priority level. An adaptive priority list establishing step is performed to execute a classification software module, and the classification software module establishes an adaptive priority list according to the priority levels of the network sessions. The communication equipment transmits the network packets of each of the network sessions to a network according to the adaptive priority list so as to set QoS of the network sessions.

Abstract: A cockpit display system includes a cockpit, a first display apparatus, a second display apparatus, a first light sensor, a second light sensor and a brightness distribution calculation module. The first light sensor is suitable for detecting a first ambient light brightness. The second light sensor is suitable for detecting a second ambient light brightness. The brightness distribution calculation module is suitable for respectively calculating a first brightness, a second brightness, a third brightness and a fourth brightness of the first display area and the second display area of the first display apparatus and the third display area and the fourth display area of the second display apparatus under a same display gray level according to the first ambient light brightness and the second ambient light brightness. The first brightness, the second brightness, the third brightness and the fourth brightness are different from each other.

Abstract: The present invention relates to a colorant set which comprises a first colorant composed of a warm color solution, a second colorant composed of a cold color solution, and a third colorant composed of a light-transmitting solution, or the colorant set only comprises the first and second colorants, and the light-transmitting solution can be added directly into the first or second colorant. The warm color solution contains 0.1 wt % to 5 wt % of water-soluble iron salt and water; the cold color solution contains 0.01 wt % to 2 wt % of water-soluble cobalt salt and water; the light-transmitting solution contains 10 wt % to 50 wt % of water-soluble yttrium or lanthanum salt and water. Accordingly, during a three-dimensional lamination molding process, the light transmittance is adjusted while spraying and coloring are performed on each layer, thereby obtaining a ceramic denture with quite natural color and light transmittance.

Abstract: In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network.

Abstract: In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network.

Abstract: A method includes forming a gate stack on a first portion of a semiconductor substrate, removing a second portion of the semiconductor substrate on a side of the gate stack to form a recess, growing a semiconductor region starting from the recess, implanting the semiconductor region with an impurity, and performing a melt anneal on the semiconductor region. At least a portion of the semiconductor region is molten during the melt anneal.

Abstract: A method includes forming a gate stack on a first portion of a semiconductor substrate, removing a second portion of the semiconductor substrate on a side of the gate stack to form a recess, growing a semiconductor region starting from the recess, implanting the semiconductor region with an impurity, and performing a melt anneal on the semiconductor region. At least a portion of the semiconductor region is molten during the melt anneal.

Abstract: A communication equipment receives a plurality of network sessions and includes a memory and a processor. The memory stores a plurality of software modules. The processor is connected to the memory and configured to implement the following steps. A network session inferring step is performed to execute an inference software module, and the inference software module processes at least one network packet of each of the network sessions and a packet characteristic module according to a machine learning algorithm to infer a priority level. An adaptive priority list establishing step is performed to execute a classification software module, and the classification software module establishes an adaptive priority list according to the priority levels of the network sessions. The communication equipment transmits the network packets of each of the network sessions to a network according to the adaptive priority list so as to set QoS of the network sessions.

Abstract: In various embodiments, an image preprocessing application preprocesses images. To preprocess an image, the image preprocessing application executes a trained machine learning model on first data corresponding to both the image and a first set of components of a luma-chroma color space to generate first preprocessed data. The image preprocessing application executes at least a different trained machine learning model or a non-machine learning algorithm on second data corresponding to both the image and a second set of components of the luma-chroma color space to generate second preprocessed data. Subsequently, the image preprocessing application aggregates at least the first preprocessed data and the second preprocessed data to generate a preprocessed image.

Abstract: A quality of service adjusting method based on application categories is configured to adjust a Quality of Service (QoS) of a communication device. A network session processing step is performed to drive a hardware accelerator to process a plurality of network sessions. A packet receiving step is performed to drive a processing unit to receive a first packet group of each of the network sessions. A packet analyzing step is performed to execute a packet inspecting module to analyze a plurality of packet data of the first packet group of each of the network sessions, and to classify an application corresponding to each of the network sessions. A list establishing step is performed to establish a priority list. A bandwidth distributing step is performed to distribute a network bandwidth to a second packet group of each of the network sessions according to the priority list.

Abstract: In various embodiments, a training application trains a machine learning model to preprocess images. In operation, the training application computes a chroma sampling factor based on a downscaling factor and a chroma subsampling ratio. The training application executes a machine learning model that is associated with the chroma sampling factor on data that corresponds to both an image and a first chroma component to generate preprocessed data corresponding to the first chroma component. Based on the preprocessed data, the training application updates at least one parameter of the machine learning model to generate a trained machine learning model that is associated with the first chroma component.

Abstract: In various embodiments, a training application generates a perceptual video model. The training application computes a first feature value for a first feature included in a feature vector based on a first color component associated with a first reconstructed training video. The training application also computes a second feature value for a second feature included in the feature vector based on a first brightness component associated with the first reconstructed training video. Subsequently, the training application performs one or more machine learning operations based on the first feature value, the second feature value, and a first subjective quality score for the first reconstructed training video to generate a trained perceptual quality model. The trained perceptual quality model maps a feature value vector for the feature vector to a perceptual quality score.

Abstract: In various embodiments, a quality inference application estimates the perceived quality of reconstructed videos. The quality inference application computes a first feature value for a first feature included in a feature vector based on a color component associated with a reconstructed video. The quality inference application also computes a second feature value for a second feature included in the feature vector based on a brightness component associated with the reconstructed video. Subsequently, the quality inference application computes a perceptual quality score based on the first feature value and the second feature value. The perceptual quality score indicates a level of visual quality associated with at least one frame included in the reconstructed video.

Abstract: In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network.