5 results about "Mobility model" patented technology

This invention discloses a hierarchical mobility management method and system for satellite-ground collaboration in low-Earth orbit (LEO) satellite networks. It primarily addresses the problems of excessive mobility signaling overhead and over-reliance on ground stations leading to network robustness issues in existing technologies for user mobility management. The solution involves: selecting key nodes for deploying Mobility Management Functions (AMFs) through network deployment planning to eliminate the maintenance overhead of virtual clusters; decoupling location updates from high-speed satellite movement by constructing dynamic activity areas for users and implementing a lazy location update strategy; establishing a mapping relationship between user identifiers and multiple nodes to achieve distributed on-board storage of user data, avoiding strong dependence on ground stations; and establishing a hierarchical paging mechanism, calculating the PCI index based on the user mobility model, and dynamically selecting paging strategies to achieve an optimal balance between paging overhead and paging latency. This invention can significantly reduce the overall signaling interaction cost while ensuring service continuity and eventual data consistency, effectively improving the robustness and autonomous operation efficiency of large-scale LEO satellite networks in scenarios with limited satellite-ground links, and can be applied to satellite communication networks.

Methods of predicting mobility of dog movement, under the control of at least one processor, can include collecting movement sensor data from a wearable monitoring device positioned on a dog, wherein the wearable monitoring device includes an accelerometer and a gyroscope (each capable of collecting three axial signals), a processor, and a memory storing instructions that, when executed by the processor, accumulates the movement sensor data in time windows. The method further includes classifying movement behavior as a binary of walk or trot within at least a plurality of the time windows based on the movement sensor data collected from multiple axes of the accelerometer and multiple axes of the gyroscope, and predicting mobility of dog movement based on the movement behaviors as applied to a trained artificial intelligence mobility model. Predicting the mobility of the dog movement can include predicting a binary of healthy mobility or compromised mobility.

This invention discloses a design method for a highly sensitive graphene temperature sensor, belonging to the field of micro-nano sensing technology. The method includes the following steps: S1, constructing a graphene geometric structure model and optimizing the structure; S2, introducing electron-phonon coupling to optimize the structure; S3, calculating the band structure based on the optimized structure; S4, calculating the temperature at a given temperature based on the band data. v F S5, based on v F S6. Calculate resistivity; S7. Set different thermodynamic temperatures and repeat steps S2 to S5; S8. Fit the resistivity-temperature change curve and calculate the temperature coefficient of resistance; S9. Construct a high-sensitivity graphene temperature sensor based on the temperature coefficient of resistance. The method of this invention does not rely on mobility models or empirical fitting parameters. It can self-consistently and quantitatively predict the temperature resistance characteristics of graphene based on the intrinsic response of the electronic structure, providing a theoretical basis for the design of high-sensitivity graphene temperature sensors.

Methods of predicting mobility of dog movement, under the control of at least one processor, can include collecting movement sensor data from a wearable monitoring device positioned on a dog, wherein the wearable monitoring device includes an accelerometer and a gyroscope (each capable of collecting three axial signals), a processor, and a memory storing instructions that, when executed by the processor, accumulates the movement sensor data in time windows. The method further includes classifying movement behavior as a binary of walk or trot within at least a plurality of the time windows based on the movement sensor data collected from multiple axes of the accelerometer and multiple axes of the gyroscope, and predicting mobility of dog movement based on the movement behaviors as applied to a trained artificial intelligence mobility model. Predicting the mobility of the dog movement can include predicting a binary of healthy mobility or compromised mobility.

This invention relates to a method for optimizing the characteristics of AlGaN / GaN high electron mobility transistor devices by determining the polarized Coulomb field scattering intensity based on the calculated average momentum relaxation rate, belonging to the field of transistor device research technology. Starting from actual test results, this invention utilizes commercial semiconductor device simulation software to solve complex and coupled mathematical and physical equations. Through Monte Carlo simulation, it continuously optimizes the strong-field mobility model in the software, iteratively deriving simulation results consistent with actual devices. Based on this, the average momentum relaxation rate of polarized Coulomb field scattering is calculated. Targeted analysis is used to verify the feasibility of the method, thereby deriving the scattering intensity law of polarized Coulomb field scattering under strong fields, and predicting and optimizing the device performance of AlGaN / GaN HEMTs.