44results about How to "Fix implementation issues" patented technology

The method comprises: by not opening the power of slave control mode, the mobile terminal is let directly enter into a master control mode single standby state; when the mobile terminal is at running state, by closing the power of slave control mode, the mobile terminal is let enter into a master control mode single standby state from a dual standby state or a slave mode single standby state. The invention can satisfy the needs of realizing a master control mode single standby state from the dual-mode dual-standby mobile terminals.

A technology relates to a satellite-based navigation positioning system, in particular to a high sensitivity auxiliary positioning system and a data processing method based on mobile communication auxiliary mode and mainly used in a ground receiver system. The system at least comprises a server, an interface, an antenna, a receiver, a code phase extraction unit and user receiver modules, wherein,all modules are combined into an integral receiver. The method realizes positioning service of the entire system under weak signal conditions by means of the modules such as the auxiliary informationextraction unit in a user receiver; moreover, the method mainly solves related technical problems such as the organic combination of integral hardware and the realization of data extraction and calculation. The invention has the advantages that: data extraction and user position calculation are realized by means of auxiliary information under the condition of very low signal-to-noise ratio and ineffective signal tracking; moreover, the invention improves the sensitivity index of the user receiver and can enlarge the application range of a navigation positioning system.

The present invention provides a QoS (Quality of Service) implementation method and device. The method comprises: a flow classification node obtains the characteristic information of a first message when receiving the first message, and obtain the priority information of the first message according to the characteristic information of the first message; the flow classification node packages a tunnel head including service chain information for the first message, and adds the priority information in the field corresponding to the service chain information of the tunnel head, and a second message is obtained; and the flow classification node send the second message through the priority information to allow the node receiving the second message to send the second message through the priority information carried in the tunnel head. According to the technical scheme of the invention, the QoS is realized, the realization problem is solved of the QoS in an Overlay network, the networking requirement of the Overlay network is satisfied, and the user experience is guaranteed.

The invention relates to a method for realizing great circle flight of an aircraft in constant height between isometric waypoints. The method is characterized by comprising the following steps of: providing specific implementation steps for realizing great circle flight of the aircraft in constant height between two isometric points through calculating real-time offset between an actual route and an ideal great circle route; and further expanding to great circle flight of the aircraft in constant height between the isometric waypoints. Through carrying out simulation verification on the method, the problem of realization of great circle flight of the aircraft in constant height can be well solved through regulating real-time height and real-time direction of the aircraft, the accuracy is high, and the requirement on mobility of the aircraft is low, so that the method can be applied to various research objects.

The present invention is applicable to the technical field of image registration, and provides a method for constructing a dual-mode deep learning descriptor based on graphics primitives. In this method, the attribute category of the patch image is learned by labeling samples, and the geometry of the patch image is learned by using the graphics primitive. Features, the attribute category and geometric features are fused to obtain the feature vector of the local patch image, that is, the descriptor based on the graphics primitive. The registration between patches is completed through the similarity of descriptor vectors, and the classification and description based on machine learning descriptors are realized. The dual-mode deep learning descriptor construction method based on graphics primitives proposed by the present invention is aimed at the classic image registration method CPU. Disadvantages of a large amount of calculation, explore the descriptor classification method of GPU (image processor) calculation. It mainly establishes a descriptor training set, builds a multi-mode convolutional network, trains categories and geometric modes on the GPU, and realizes classification and registration of local patch images. Solve the classification description method of the descriptor and the implementation problem on the GPU.