Abstract: The present disclosure in some embodiments provides a system for and a method of providing a live video in real-time by transmitting the live video and patches which are a fraction of a frame of the live video by allocating and using bandwidth for transmitting the live video and bandwidth for transmitting the patches, respectively, and by subjecting, based on the patches, a deep neural network-based super-resolution model to online learning and thereby super-resolution processing the live video into a super-resolution live video.

Abstract: The present disclosure in some embodiments provides a system for and a method of providing a live video in real-time by transmitting the live video and patches which are a fraction of a frame of the live video by allocating and using bandwidth for transmitting the live video and bandwidth for transmitting the patches, respectively, and by subjecting, based on the patches, a deep neural network-based super-resolution model to online learning and thereby super-resolution processing the live video into a super-resolution live video.

Abstract: The present disclosure seeks to perform real-time video streaming on a mobile device toward maintaining user QoE even under bandwidth constraints while being acceptable to the lightweight computing capacity of the mobile device. To this end, the embodiments apply deep neural network-based SR to a small number of pre-selected video frames and utilize the video frames to which SR is applied to enhance the resolution of the remaining frames, wherein the pre-selected frames are chosen for SR within a preset quality margin. Additionally, the present disclosure provides an apparatus and a method for SR acceleration for real-time video streaming under the lightweight computing capacity and video-specific constraints of a mobile device, which allow a server to deliver multiple options on a deep neural network and a cache profile including SR application information and enable the mobile device to select an option suitable for its computing capacity.

Abstract: The present disclosure seeks to perform real-time video streaming on a mobile device toward maintaining user QoE even under bandwidth constraints while being acceptable to the lightweight computing capacity of the mobile device. To this end, the embodiments apply deep neural network-based SR to a small number of pre-selected video frames and utilize the video frames to which SR is applied to enhance the resolution of the remaining frames, wherein the pre-selected frames are chosen for SR within a preset quality margin. Additionally, the present disclosure provides an apparatus and a method for SR acceleration for real-time video streaming under the lightweight computing capacity and video-specific constraints of a mobile device, which allow a server to deliver multiple options on a deep neural network and a cache profile including SR application information and enable the mobile device to select an option suitable for its computing capacity.

Abstract: A semiconductor device and a system including the semiconductor device are disclosed, which relate to a technology for detecting a defective or failed part during a probe test of the semiconductor device. The semiconductor device includes a test controller configured to perform counting of a read flag signal during activation of a test signal and to control a data mask signal to be toggled at an N-th activation time of the read flag signal. The semiconductor device further includes a cell array configured to receive and store an output signal of the test controller through a data line during a write operation and to output the stored data to a test device during a read operation.

Abstract: A semiconductor device and a system including the semiconductor device are disclosed, which relate to a technology for detecting a defective or failed part during a probe test of the semiconductor device. The semiconductor device includes a test controller configured to perform counting of a read flag signal during activation of a test signal and to control a data mask signal to be toggled at an N-th activation time of the read flag signal. The semiconductor device further includes a cell array configured to receive and store an output signal of the test controller through a data line during a write operation and to output the stored data to a test device during a read operation.

Abstract: A semiconductor device may be provided. The semiconductor device may include a signal mixing circuit suitable for generating a strobe signal which toggles in synchronization with a divided clock. The semiconductor device may include a signal transfer circuit suitable for transmitting the strobe signal, and including at least one repeater which amplifies and transmits the strobe signal.

Abstract: A semiconductor memory apparatus may be configured to, in a data reference voltage training mode, set a reference pad reference voltage by training a first initial data reference voltage for a reference pad being any one of a plurality of input/output pads, and set a data reference voltage for each of remaining input/output pads by training a second initial data reference voltage being the reference pad reference voltage for each of the remaining input/output pads.

Abstract: A semiconductor memory apparatus may be configured to, in a data reference voltage training mode, set a reference pad reference voltage by training a first initial data reference voltage for a reference pad being any one of a plurality of input/output pads, and set a data reference voltage for each of remaining input/output pads by training a second initial data reference voltage being the reference pad reference voltage for each of the remaining input/output pads.