ASUSTeK Computer

The auxiliary power device (100) provided includes a fixing base (110) disposed on a seat tube (210) of a bicycle (200), a main body (120), a driving module (130), and two positioning rods (140). The driving module (130) includes a motor (131) with a rotating shaft in a center and a driving wheel (132) covering around the motor (131) and adapted to be in contact with a rear wheel (220) of the bicycle (200). Each of the two positioning rods (140) has a first end (E1) and a second end (E2). The two first ends (E1) are connected to the main body (120), and the two second ends (E2) are rotatably connected to two opposite ends of the rotating shaft respectively.

An abdominal image-based analysis system (10) and a method thereof are provided. The analysis system (10) includes a computing device (12) and a display device (16). In the analysis system (10), a regression deep learning model (14) is implemented into the computing device (12). The computing device (12) receives at least one abdominal image (18) and inputs the abdominal image (18) into the regression deep learning model (14). The regression deep learning model (14) extracts a physiological characteristic value in the abdominal image (18) and executes an analysis and regression operation to generate a predicted physiological index (20). The display device (16) is electrically connected to the computing device (12) to display the abdominal image (18) and the corresponding predicted physiological index (20). Therefore, the abdominal image (18) is combined with artificial intelligence (AI) technology to generate an objective predictive physiological index (20) to assist doctors in analysis.

A dimension evaluation system (10) includes a computing device (12) and a display device (14). An anomaly detection model (16), a three-dimensional feature prediction model (18), and a feature tracking prediction model (20) are built in the computing device (12). The anomaly detection model (16) detects an anomalous feature (28) on an instant image (26), to mark a selection box (30), and obtains location information. The three-dimensional feature prediction model (18) calculates a dimension of the anomalous feature (28) based on the instant image (26) and the location information. The feature tracking prediction model (20) performs mathematical statistics on all dimensions of the anomalous feature (28) when it is determined that anomalous features (28) detected in two consecutive pictures of the instant image (26) are the same, and generates a mathematical statistics result (32) of the dimension of the anomalous feature (28) when the computing device (12) receives a picture static signal. The display device (14) displays an instant image (26), a selection box (30), and a mathematical statistics result (32).

An image processing method and an electronic device (10) are provided. The electronic device (10) performs the image processing method. The image processing method includes: inputting a low-resolution image into a super-resolution model (121); using the super-resolution model (121) to perform an interpolation process on the low-resolution image to generate a first intermediate image; using the super-resolution model (121) to perform a convolution activation process on the low-resolution image to generate a second intermediate image; and performing an addition operation on the first intermediate image and the second intermediate image to generate a high-resolution image.

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Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

In an example, a User Equipment, UE, receives and/or detects a transmission of data from a network. The transmission of the data is addressed to a Group Radio Network Temporary Identifier, G-RNTI, for a Hybrid Automatic Repeat Request, HARQ, process (905). In response to receiving and/or detecting the transmission of the data, the UE (i) starts a first HARQ Round Trip Time, RTT, timer associated with the G-RNTI for the HARQ process, and (ii) starts a second HARQ RTT timer associated with a Cell Radio Network Temporary Identifier, C-RNTI, for the HARQ process (910). The UE starts a multicast retransmission timer for the HARQ process in response to expiry of the first HARQ RTT timer (915). The UE starts a unicast retransmission timer for the HARQ process in response to expiry of the second HARQ RTT timer (920). The UE receives and/or detects a first retransmission of the DL data addressed to the G-RNTI for the HARQ process (925). In response to receiving and/or detecting the first retransmission of the data, the UE (i) stops the multicast retransmission timer, and (ii) stops the unicast retransmission timer (930).

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A method and apparatus are disclosed from the perspective of a UE. In one embodiment, the method includes the UE receiving a configuration which indicates functionalities of each symbol within a set of symbols (2105). The method also includes the UE determining a slot structure for a slot according to a group common PDCCH associated with the slot and the configuration (2110).