Abstract: A user equipment (UE) includes one or more non-transitory computer-readable media containing computer-executable instructions embodied therein, and at least one processor coupled to the one or more non-transitory computer-readable media. The at least one processor configured to execute the computer-executable instructions to receive downlink control information (DCI) on a downlink (DL) channel of a non-terrestrial network (NTN), the DL channel reception ending in a first slot, and transmit an uplink (UL) transmission on a UL channel of the NTN in a second slot. The second slot is separate from the first slot by a timing offset, where a duration of the timing offset is dependent on a type of the UL transmission and a numerology of the UL transmission.

Abstract: Disclosed are an image display method and a 3d display system. The method is adapted to the 3d display system including a 3d display device and includes the following steps. A first image and a second image are obtained by splitting an input image according to a 3d image format. Whether the input image is a 3D format image complying with the 3D image format is determined through a stereo matching processing performed on the first image and the second image. An image interweaving process is enabled to be performed on the input image to generate an interweaving image in response to determining that the input image is the 3D format image complying with the 3D image format, and the interweaving image is displayed via the 3D display device.

Abstract: A method of manufacturing a semiconductor device includes forming a plurality of fin structures extending in a first direction over a semiconductor substrate. Each fin structure includes a first region proximate to the semiconductor substrate and a second region distal to the semiconductor substrate. An electrically conductive layer is formed between the first regions of a first adjacent pair of fin structures. A gate electrode structure is formed extending in a second direction substantially perpendicular to the first direction over the fin structure second region, and a metallization layer including at least one conductive line is formed over the gate electrode structure.

Abstract: A 3D format image detection method and an electronic apparatus using the same are provided. The 3D format image detection method includes the following steps. A first image and a second image are obtained by splitting an input image according to a 3D image format. A 3D matching processing is performed on the first image and the second image to generate a disparity map of the first image and the second image. The matching number of a plurality of first pixels in the first image matched with a plurality of second pixels in the second image is calculated according to the disparity map. Whether the input image is a 3D format image conforming to the 3D image format is determined according to the matching number.

Abstract: A training data generation device includes a virtual scene generation unit, an orthographic virtual camera, an object-occlusion determination unit, an object-occlusion determination unit and a perspective virtual camera. The virtual scene generation unit is configured for generating a virtual scene, wherein the virtual scene comprises a plurality of objects. The orthographic virtual camera is configured for capturing a vertical projection image of the virtual scene. The object-occlusion determination unit is configured for labeling an occluded-state of each object according to the vertical projection image. The perspective virtual camera is configured for capturing a perspective projection image of the virtual scene. The training data generation unit is configured for generating a training data of the virtual scene according to the perspective projection image and the occluded-state of each object.

Abstract: An object pose estimation system, an execution method thereof and a graphic user interface are provided. The execution method of the object pose estimation system includes the following steps. A feature extraction strategy of a pose estimation unit is determined by a feature extraction strategy neural network model according to a scene point cloud. According to the feature extraction strategy, a model feature is extracted from a 3D model of an object and a scene feature is extracted from the scene point cloud by the pose estimation unit. The model feature is compared with the scene feature by the pose estimation unit to obtain an estimated pose of the object.