Due to the existence of a large number of different brands and models of equipment and systems in the power system, the CPU control system in the power system is prone to system integration and compatibility problems. In order to ensure the smooth application of CPU control system in electric power system, the research on the application of CPU control system in electric power system based on ARM processor is proposed. The CPU control system architecture of ARM processor is established, and the irregular characteristics and task attributes of computational tasks such as sparse matrix operations in power system topology analysis are analyzed. Subsequently, a general optimization criterion for GPU parallel algorithm design is proposed. Experimental results show that the above CPU control system for topology analysis algorithms in power systems achieves a 6-fold acceleration ratio relative to the 8-threaded CPU algorithm on a 30,000-node system, and more than a 4-fold acceleration in the full process of topology analysis.
The need for real-time processing of multi-frame images with complex geometric and optical corrections makes the algorithm more computationally intensive and requires higher processor performance. In order to ensure the real-time and resource consumption problems of the multi-motion target monocular vision multi-level detection algorithm in practical applications, a multi-motion target monocular vision multi-level detection algorithm for ARM isomorphic processors is proposed. The algorithm groups multiple cores in an isomorphic multicore processor evenly, so that the processor cores in the same group are a processor cluster set. The tasks to be scheduled are arranged in ascending order of utilization, and the tasks are assigned to each processor cluster set based on the worst-case adaptation algorithm, and it is guaranteed that the tasks with greater utilization than in each cluster set do not exceed the number of cores in the cluster set. In addition, the moving targets are separated from the background using methods such as background subtraction, inter-frame differencing, or optical flow. The detected targets are matched with their positions in the preceding and following frames to track the motion trajectory. Detection is performed at different resolution levels to handle targets of different sizes and velocities. The experimental results show that the algorithm can utilize the computing resources of the processor more efficiently, achieve faster target detection and tracking, and meet the needs of real-time applications.
QR code is widely used in many scenarios including online transactions, warehousing, manufacturing and authentication. However, in the era of continuous development of quantum technology, the traditional QR code encryption method has low security in front of quantum technology, and information is easy to be stolen and forged. In response to the above problems, we consider combining post-quantum cryptography with QR code. In this work, we propose a quantum-safe, anti-counterfeiting and tamper-proof QR code based on lattice-based cryptography. The proposed QR code technique features two advantages: (a) it can be easily deployed on mobile phone apps and the verification is computationally efficient; (b) it is safe under quantum attacks and impossible to be forged and imitated in plausible quantum future.
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