Abstract: A method for measurement-occasion determination, a terminal device, and a chip are provided in the disclosure. The method includes the following. A terminal device determines a measurement occasion of a first measurement object (MO) in at least one measurement occasion according to whether a measurement gap (MG) and/or a network control small gap (NCSG) are needed for measurement of the first MO. The at least one measurement occasion includes the NCSG.

Abstract: A connecting terminal includes a bottom plate having a plurality of bumps and/or a plurality of recesses arranged on a surface of the bottom plate, a first arm extending from a first end of the first arm to a second end of the first arm, and a second arm extending from a first end of the second arm to a second end of the second arm. The first end of the first arm and the first end of the second arm are respectively coupled to a pair of sides of the bottom plate, and the first arm, the bottom plate, and the second arm form a space for accommodating a wire. The bumps and/or the recesses scrape an insulated layer of a wire and form an electrical connection with the wire.

Abstract: A wireless communication method, a terminal device and a network device are provided. A terminal device transmits first information to a network device, the first information indicating a parameter the terminal device expects the network device to configure for a Multimedia Broadcast Multicast Service (MBMS) transmission. The terminal device reports an expected parameter for transmitting an MBMS service, such that the network device can provide the MBMS service based on the expected parameter of the terminal device, which is beneficial to meet the service requirements of the terminal device.

Abstract: A technique for providing search results may include determining a first entity type, a second entity type, and a relationship type based on a compositional query. The technique may also include identifying nodes of a knowledge graph corresponding to entity references of the first entity type and entity references of the second entity type. The technique may also include determining from the knowledge graph an attribute value corresponding to the relationship type for each entity reference of the first entity type and for each entity reference of the second entity type. The technique may also include comparing the attribute value of each entity reference of the first entity type with the attribute value of each entity reference of the second entity type. The technique may also include determining one or more resultant entity references from the entity references of the first entity type based on the comparing.

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.