Beijing Glass Research Institute

This report analyzes the radio compatibility between 5 G systems in the 1800 MHz band and broadband TDD private networks in China, focusing on signal interference and its impact on network performance. It presents solutions like optimizing frequency allocation, implementing advanced interference management, and enhancing network capacity to improve communication quality and minimize interference. The report underscores the importance of continuous data analysis in driving technological innovation and network improvement. It highlights how understanding and addressing signal interference is crucial for enhancing user experience and supporting the future development of communications. The report emphasizes the significance of data-driven decision-making in the communications industry, suggesting that ongoing data analysis and adaptation to changing technologies and market demands will lead to more reliable and efficient communication services and technological advancements.

The rapid evolution of wireless communication technologies has led to the emergence of 6G networks, promising unprecedented connectivity and data processing capabilities. Within the 6G landscape, vehicle edge computing has gained increasing importance due to its potential to enhance real-time applications and services for connected and autonomous vehicles. However, the seamless integration of vehicles into edge computing environments presents a significant challenge—balancing the trade-off between energy consumption and latency. This research paper explores the critical problem of achieving an equilibrium between energy efficiency and low-latency communication in vehicle edge computing within the context of 6G networks. We propose a novel approach that integrates energy-aware computing strategies and latency optimization techniques to strike the optimal balance between these two conflicting objectives. In the methodology section, we provide a comprehensive overview of our proposed approach, detailing how it measures and evaluates both energy consumption and latency in the context of vehicle-edge interactions. We leverage advanced algorithms and models to efficiently allocate computing resources, optimize task scheduling, and minimize data transmission delays, all while considering the energy constraints of the vehicular devices. To validate the effectiveness of our approach, extensive experiments were conducted in a simulated vehicular edge computing environment. The experimental results demonstrate a substantial reduction in energy consumption while maintaining low-latency communication, outperforming existing methods. We present these results in graphical form, offering clear visual evidence of the improvements achieved.

Zirconium oxide (ZrO2) and boron carbide (B4C) were added to ZrB2 raw powders to prepare ZrB2 porous ceramics by reactive spark plasma sintering (RSPS). The reactions between ZrO2 and B4C which produce ZrB2 and gas (such as CO and B2O3) result in pore formation. X-Ray Diffraction results indicated that the products phase was ZrB2 and the reaction was completed after the RSPS process. The porosity could be controlled by changing the ratio of synthesized ZrB2 to raw ZrB2 powders. The porosity of porous ceramics with 20 wt% and 40 wt% synthsized ZrB2 are 0.185 and 0.222, respectivly. And dense ZrB2-SiC ceramic with a porosity of 0.057 was prepared under the same conditions for comparison. The pores were homogeneously distributed within the microstructure of the porous ceramics. The results indicate a promising method for preparing porous ZrB2-based ceramics.

When, constructing the scale space of fast Hessian detector in SURF, using integral image and box filters to approximate the Gaussian second order derivative filters is the most pivotal step. The common algorithm for this is firstly calculating the pixels' sums in each sub-rectangle region of a mask, then multiplying the coefficients and adding them together. This way may cause some repeatable calculations, so the fast Hessian detector's speed is influenced. In order to make the filter process much faster, an optimized algorithm for Gaussian second order derivative filters was proposed. Firstly, the multiplication operators was all replaced by additions. Then, use variants instead of repeatable steps. At last, complexity of the whole convolution process was decreased. Verified by the experimental results, fast Hessian detector based on the optimized filter algorithm was much faster than that on the common method.