Abstract: A method for determining a measurement period includes: determining, by a terminal device, a measurement period of a first frequency point and/or a first MO based on a first positional relationship; where the first positional relationship includes a positional relationship between at least some MGs of a plurality of configured MGs and measurement timing windows of the first frequency point and/or the first MO, and/or a positional relationship between third MGs of the plurality of MGs that are used to measure the first frequency point and/or the first MO and remaining MGs of the plurality of MGs except the third MGs.

Abstract: Provided is a method for wireless communication.

Abstract: A method for measurement gap (MG) enhancement, a terminal device, and a computer-readable storage medium are provided in embodiments of the disclosure. The method includes the following. A terminal device determines a user equipment (UE) capability of the terminal device, where the UE capability indicates whether the terminal device supports a pre-configured MG (pre-MG) activation/deactivation (A/D) capability in a specific manner and/or a specific scenario.

Abstract: An information transmission method, a terminal device, and a network device are provided. The terminal device includes: a processor, and a memory storing instructions executable by the processor. When the instructions are executed by the processor, the terminal device is caused to: in a case where Network Control Small Gap (NCSG) is supported, send first indication information to a network device. The first indication information is used to indicate NCSG configuration information supported by the terminal device.

Abstract: A display panel, which belongs to the technical field of display devices. The display panel includes an opening area (20) provided with an opening: an active area (70) at least partially surrounding the opening area (20); and a first anti-crack area (40). The display panel in the first anti-crack area (40) further includes: at least one circle of anti-crack protective layer (41); the active area (70) includes at least one touch electrode layer (71); and the anti-crack protective layer (41) and at least one of the at least one touch electrode layer (71) are arranged in a same layer and with a same material. The anti-crack protective layer (41) of the display panel of the present disclosure may improve the anti-crack ability of the area surrounding the digging holes.

Abstract: Provided are a training method of a deep convolutional neural network, an abnormal behavior monitoring method and system, a storage medium and computer device. The deep convolutional neural network is a single-stage dual-branch convolutional neural network, and includes a first branch for predicting confidences and a second branch for predicting part affinity vector fields. The method includes: inputting an image to be identified; according to one or more preset objects to be identified, performing feature analysis on the image to be identified to obtain one or more feature map sets for the one or more objects to be identified in the image to be identified, wherein each feature map set corresponds to one object to be identified; inputting a feature map set into the first branch to obtain confidence prediction results; inputting the confidence prediction results and feature map set into the second branch to obtain affinity field prediction results.

Abstract: A detection method, a detection device, a terminal, and a detection system are provided for detecting a state of a target object in a monitoring area. The detection method includes: acquiring point cloud data obtained based on a millimeter-wave radar signal in the monitoring area and preprocessing the point cloud data; extracting features of the preprocessed point cloud data through a stacked auto-encoder network to obtain output data; classifying the output data by a classifier to obtain a classification result; and determining the state of the target object in the monitoring area according to the classification result. A better detection effect can be ensured on the basis of protecting privacy of user.

Abstract: A detection method, a detection device, a terminal, and a detection system are provided, for detecting a state of a target object in a detection area. The detection method includes: filtering a millimeter-wave radar signal received in the detection area; and extracting features suitable for indicating a motion mode of the target object in the detection area from each frame of the filtered millimeter-wave radar signal; monitoring an initial change point of the features through a flow window; caching a predetermined number of features starting from the initial change point; and identifying the cached features by a classifier to determine the state of the target object in the detection area.

Abstract: A fall detection method, device, and system are disclosed in this application for detecting whether a target object falls in a detection area. The fall detection method includes: receiving a WIFI signal transmitted by a transmitter in the detection area and extracting CSI data from the WIFI signal; preprocessing the CSI data to obtain CSI data to be identified, and processing the CSI data to be identified through a deep neural network to determine whether the target object falls in the detection area. In this application, a deep neural network is adopted to perform fall detection and the detection accuracy is improved.