5results about How to "Perceived effect is good" patented technology

The application belongs to the technical field of wireless communication and signal processing, and discloses a quantum heuristic joint optimization method suitable for a flow state antenna ISAC system, which comprises the following steps: 1, a MIMO-FAS model is constructed, and an equivalent downlink channel is given to realize unified expression of a communication link and a sensing link; 2, a weighted sum rate maximization problem P1, a minimum user SINR maximization problem P2 and a sensing signal-to-jamming noise ratio SCNR maximization problem P3 are constructed; 3, the weighted sum rate maximization problem P1 is solved; 4, the minimum user SINR maximization problem P2 is solved; 5, the sensing signal-to-jamming noise ratio SCNR maximization problem P3 is solved; and 6, a quantum heuristic simulation bifurcation method is adopted to perform port selection optimization to obtain a joint optimal port-beam configuration. The application realizes collaborative optimization of port configuration and beam design, effectively improves the communication rate and sensing accuracy of the system while guaranteeing the calculation efficiency and scalability.

According to the information transmission method and device for vehicle monitoring and the computer readable medium provided by the embodiment of the invention, an authentication mechanism of fingerprint information is constructed based on hardware information, and the fingerprint information is sensitive to changes of hardware such as a mainboard and a network card; therefore, it is ensured that only the authenticated vehicle-mounted equipment can perform heartbeat interaction with the server-side equipment and report data, the authenticity of a data source is ensured, data security risks caused by equipment cloning and the like can be prevented, a heartbeat interaction mechanism and a data reporting process are optimized, and the data reporting efficiency is improved. The problems that in an existing scheme, a large number of network resources are consumed in an unstable network environment, resource waste is caused, and data loss is prone to occurring are effectively solved.

This invention discloses a deep learning-based super-resolution reconstruction method for polarization images, which can be used to reconstruct corresponding high-resolution polarization images from low-resolution polarization images. The method includes: acquiring high-resolution polarization images using a polarization camera and preprocessing them to obtain low-resolution polarization images, creating a dataset; fusing feature information through a shallow convolutional neural network; performing dense feature fusion through a residual dense module; using an upscaling module to amplify the number of image feature channels to a corresponding magnification factor; obtaining the super-resolution polarization image through an output module; and optimizing the neural network using a polarization-aware and pixel-mixing loss function. This method, based on a deep learning convolutional neural network, constructs a super-resolution reconstruction neural network model and uses a polarization-aware and pixel-mixing loss function to train and optimize the neural network, enabling the reconstruction of super-resolution polarization images from low-resolution polarization images.

The utility model relates to the technical field of fan quality inspection equipment, and discloses fan blade rotation detection equipment, which comprises a detection mechanism, the detection mechanism comprises an annular seat, a driving mechanism is arranged in the middle of the annular seat, a detection analysis mechanism is arranged at the top end of the annular seat, and the driving mechanism comprises a driving motor arranged at the central position in the annular seat. And the driving end above the driving motor is fixedly connected with a fan blade mounting mechanism. According to the utility model, the high-precision sensing detection structure design is adopted, the optical fiber sensors with higher sensitivity and resolution ratio are selected to replace the traditional strain gauge type sensors, the optical fiber sensors have better sensing ability to micro strain and displacement changes, and the four optical fiber sensors are arranged at the same time, so that the detection accuracy is improved; the four optical fiber sensors are arranged below the fan blade mounting mechanism of the driving mechanism in an equal-angle shape, and the data processing host is arranged in a matched mode and connected with the four optical fiber sensors through connecting wires, so that the detection precision can be improved.

This invention discloses an AR multi-person collaborative interactive projection system and method for public spaces, belonging to the field of AR multi-person collaborative interactive projection technology. It includes a multi-source spatial calibration module configured to extract static physical feature point clouds in the public space based on real-time environmental images and depth data collected by multiple AR devices, and to perform global registration of the feature point clouds using a weighted nearest neighbor iterative algorithm to generate a unified world coordinate system. This invention utilizes the multi-source spatial calibration module to perform global registration of static physical feature point clouds collected by multiple AR devices using a weighted nearest neighbor iterative algorithm to generate a unified world coordinate system, and detects the relative pose deviation between each AR device. When the deviation exceeds a preset threshold, a dynamic recalibration process is triggered, effectively solving the problem of inconsistent coordinate systems among multiple AR devices in public spaces, and significantly improving the spatial consistency and registration accuracy of multi-person collaboration.