Photon correlation technology is an important technology in the field of nanoparticle measurement at present. This paper expounds the research significance of photon correlation technology, introduces the research background and basic concepts of this technology, and studies and analyzes the research status of the correlation algorithm of the new photon correlator. Finally, prospect and application development trend of this technology are analyzed, from the aspects of practicability, application environment and hardware conditions, the difficulties faced by the photon correlation technology are put forward. There are some difficulties when the photon correlator needs to meet the requirements of accuracy, small sampling interval and large dynamic range, this article presents some possible solutions.

With the development of Ethernet systems and the growing capacity of modern silicon technology, embedded communication networks are playing an increasingly important role in embedded and safety critical systems. Hardware/software co-design is a methodology for solving design problems in processor based embedded systems. In this work, we implemented a new 1-cycle pipeline microprocessor and a fast Ethernet transceiver and established a low cost, high performance embedded network controller, and designed a TCP/IP stack to access the Internet. We discussed the hardware/software architecture in the forepart, and then the whole system-on-a-chip on Altera Stratix EP1S25F780C6 device. Using the FPGA environment and SmartBit tester, we tested the system’s throughput. Our simulation results showed that the maximum throughput of Ethernet packets is up to 7 Mbps, that of UDP packets is up to 5.8 Mbps, and that of TCP packets is up to 3.4 Mbps, which showed that this embedded system can easily transmit basic voice and video signals through Ethernet, and that using only one chip can realize that many electronic devices access to the Internet directly and get high performance.

Metaheuristic algorithms are widely used to solve complex optimization problems, but the trade-off between exploration and exploitation often limits their performance. The Crested Porcupine Optimizer (CPO) employs four bio-inspired defense mechanisms and achieves competitive performance, but it still tends to converge prematurely, initialize populations poorly, and rely on static parameters that cannot adapt to different phases. This paper proposes a Multi-Strategy Enhanced Crested Porcupine Optimizer (MSCPO) with four phase-targeted enhancement strategies: (1) Kent Chaos Opposition-Based Learning Initialization (KCOL) improves the initial population distribution through chaotic-weighted reflection; (2) Arctic Puffin Optimization (APO)-Inspired Dual-Modal Evasion (APO-DME) reduces dependence on the global best solution and increases population diversity through dual-mode differential perturbation; (3) Adaptive Dual-Differential Perturbation (ADDP) refines the search by combining population diversity and elite guidance information; (4) Periodic Dynamic Adaptive Perturbation (PDAP) enhances exploitation through periodic trigonometric perturbation and a fitness-conditioned update rule. These strategies interact across the optimization process to strengthen each defense mechanism at the appropriate phase. On the CEC 2017 and CEC 2022 benchmark suites, MSCPO achieves the best overall mean rank among all compared algorithms, with an overall rank of 2.638 on CEC 2017 and 2.375 on CEC 2022. A full-factorial ablation over all 16 strategy combinations confirms that each strategy contributes positively: removing any single strategy degrades the overall mean rank, and the complete MSCPO achieves the best mean rank (3.34), significantly outperforming all single-strategy variants (Wilcoxon signed-rank test, p < 0.001). To verify the practical applicability of MSCPO, the algorithm is further applied to three engineering design problems: step-cone pulley design, hydrostatic thrust bearing design, and robotic gripper design. MSCPO ranks first on the hydrostatic thrust bearing problem, second on the step-cone pulley problem, and third on the robotic gripper problem. Future work will explore adaptive population sizing to further improve the scalability of MSCPO on very high-dimensional problems.