Baolin Hou

By leveraging the complementary advantages of free-space optical (FSO) and radio frequency (RF) links, hybrid FSO/RF systems exhibit broad application prospects. However, maintaining robustness while performing trade-off optimization between reliability and transmission efficiency under dynamic conditions with power constraints remains challenging. To address this, we propose a weather-aware multi-objective adaptive power allocation approach for hybrid FSO/RF systems based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a deep neural network (DNN). Closed-form expressions for the average bit-error rate (ABER) and average channel capacity (ACAP) are derived and used to evaluate NSGA-II objectives, generating a labeled optimal allocation dataset across diverse scenarios. A DNN is then trained on the dataset to learn adaptive power allocation strategies for dynamic environments. Numerical results demonstrate that the proposed scheme effectively achieves adaptive power allocation and significantly outperforms existing benchmark schemes. In dynamic scenarios, it reduces the ABER by 1–2 orders of magnitude and substantially lowers the outage probability (OP), while improving the overall ACAP by more than 0.5 Gbps under the same transmit power.

Frequency-hopping (FH) communication is widely employed in modern wireless communication systems due to its strong resistance to interference. Accurate detection of FH signals is therefore essential for effective spectrum monitoring and reliable communication in complex electromagnetic environments. However, real-world electromagnetic environments are highly complex and dynamic, with substantial noise and multiple interfering signals coexisting. These conditions pose significant challenges to frequency-hopping signal detection, particularly in terms of low signal-to-noise ratios and co-channel interference. To address these challenges, this paper proposes a frequency-hopping signal detection method based on time–frequency cancellation and the Hough transform. The received signals are first preprocessed using time–frequency cancellation and singular value decomposition to suppress noise and fixed-frequency interference. Subsequently, the time–frequency characteristics of the preprocessed signals are extracted, and the time–frequency cancellation ratio is computed to perform an initial determination of the presence of frequency-hopping signals. To further reduce false detections caused by multiple interference sources, the Hough transform is applied to analyze the time–frequency spectrum in greater detail. By jointly exploiting the geometric and statistical characteristics of the signals, accurate detection of frequency-hopping signals is achieved. Experimental results demonstrate that the proposed method enables precise detection of frequency-hopping signals under challenging electromagnetic conditions.

狙击瞄准点轨迹视觉测量系统是一种应用于现代化狙击训练领域的高精度光学测量装置，针对该系统高分辨率图像处理延迟与户外强光导致的轨迹定位困难问题，提出一种基于自适应阈值感兴趣区域（region of interest, ROI）投影法的瞄准点质心快速定位算法，并基于现场可编程门阵列（field programmable gate array, FPGA）平台硬件实现。该算法通过分析光斑像素个数在图像中的占比，结合相关场景参数动态计算ROI提取的自适应阈值，实现了ROI区域的高效精准提取，大幅减少了计算量。实验结果表明，对于2 448×2 048分辨率的图像，所提算法将处理数据量减少了98.7%。同时，自适应阈值机制有效克服了户外环境光照变化的影响，在不同射击场景下均能稳定提取质心，显著提升了系统的实时性与鲁棒性。

In low-resource speech recognition, the performance of the Whisper model is often limited by the size of the available training data. To address this challenge, this paper proposes a training optimization method for the Whisper model that integrates Low-Rank Adaptation (LoRA) with the Optuna hyperparameter optimization framework. This combined approach enables efficient fine-tuning and enhances model performance. A dual-metric early stopping strategy, based on validation loss and relative word error rate improvement, is introduced to ensure robust convergence during training. Experimental data were collected from three low-resource languages in Xinjiang, China: Uyghur, Kazakh, and Kyrgyz. Compared to baseline LoRA fine-tuning, the proposed optimization method reduces WER by 20.98%, 6.46%, and 8.72%, respectively, across the three languages. The dual-metric early stopping strategy effectively shortens optimization time while preventing overfitting. Overall, these results demonstrate that the proposed method significantly reduces both WER and computational costs, making it highly effective for low-resource speech recognition tasks.

Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time processing and is very suitable for hardware accelerators in edge computing devices. The iterative calculation method of the CORDIC algorithm yet leads to problems such as complex structure and high consumption of hardware resource. In this paper, we propose an RDP-CORDIC algorithm which pre-computes all micro-rotation directions and transforms the conventional single-stage iterative structure into a three-stage and multi-stage combined iterative structure, thereby enabling it to solve the problems of the conventional CORDIC algorithm with many iterations and high consumption. An accuracy compensation algorithm for the direction prediction constant is also proposed to solve the problem of high ROM consumption in the high precision implementation of the RDP-CORDIC algorithm. The experimental results showed that the RDP-CORDIC algorithm had faster computation speed and lower resource consumption with higher guaranteed accuracy than other CORDIC algorithms. Therefore, the RDP-CORDIC algorithm proposed in this paper may effectively increase computation performance while reducing the power and resource consumption of edge computing devices in smart grid systems.