In the context of next-generation wireless communications and multi-source heterogeneous network systems, traditional cryptographic mechanisms and security protocols pose significant risks in Internet of things(IoT) environments. There is an urgent demand for more efficient and reliable identity authentication technologies. Radio frequency fingerprinting identification (RFFI), which leverages the inherent signal characteristics of wireless devices, provides a novel approach to addressing device authentication and security challenges. Unlike existing reviews that focus on selected aspects of RFFI from a broad perspective, this paper proposes a systematic and comprehensive framework. It begins by explaining the fundamental principles and characteristics of radio frequency fingerprint (RFF). Then, from the perspectives of statistical features and deep learning (DL)-based features, the paper presents an in-depth review of RFFI classification and identification methods, along with a comparative analysis of the two approaches supported by experimental validation. Finally, several potential research directions in intelligent RFFI are discussed, and future trends of RFF technology are explored, aiming to offer both theoretical insights and practical guidance for ongoing research and real-world applications.

Hex is a perfect-information board game, and its opening library-an essential component of the game system — has traditionally been generated based on human expertise and Monte Carlo tree search (MCTS) algorithms. However, this approach is computationally expensive and may not consistently ensure accuracy. This study proposes a self-play method based on Q-learning for the efficient construction of Hex opening libraries. The proposed method employs multi-threaded simulations and an improved upper confidence bound applied to trees (UCT) algorithm to identify promising opening moves. An enhanced -greedy strategy is incorporated to improve the convergence rate of the Q-learning algorithm. To further improve performance, Q-values are integrated into the upper confidence bound(UCB) formula as prior knowledge, which is intended to enhance decision-making accuracy during gameplay. Experimental results indicate that after 3 000 training iterations, the Q-values across the board converge, suggesting the method's potential for stable policy learning. In comparative evaluations, the generated opening library achieved a 62.9% average win rate against the improved UCT algorithm. When Q-values were used as prior input to the UCB formula, the average win rate increased to 75.9%. The method was also applied in the Chinese Computer Game Competition, where the implementation received a first-place award, supporting the practical applicability of the approach.

Magnetic components play a key role in energy transfer, storage, and filtering, directly affecting the size, weight, loss, and cost of power converters. Therefore, accurate prediction of core loss is essential. To address the significant influence of excitation waveforms on core loss, an ensemble learning-based waveform classification strategy is proposed. Support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) are used as base classifiers. The classification outputs are combined with original features to construct a new feature set, which is then used to train a meta-classifier to enhance generalization. XGBoost is selected as the core model for core loss prediction. A genetic algorithm is applied for multi-objective optimization to identify the optimal operating condition with minimal core loss and maximal magnetic energy transfer. Experimental results show that the ensemble classification model can accurately classify excitation waveforms. Compared with traditional core loss prediction models and other machine learning methods, the XGBoost model demonstrates higher prediction accuracy and better regression performance. The optimized framework demonstrates the capability to meet both loss reduction and energy efficiency objectives.

To address access collisions caused by massive user access in networks, a dynamic uplink random access method based on non-orthogonal multiple access (NOMA) is proposed. The method incorporates an adaptive resource allocation mechanism that dynamically adjusts time slot configurations and updates the access class barring (ACB) factor according to network conditions. After each round of random access, the system records the number of idle, successful, and collided time slots. These statistics are then used to estimate the number of remaining users. The process is iteratively repeated until all users successfully access the network. To evaluate the proposed method, a complete theoretical analysis framework is established under the Nakagami-m fading channel environment, from which closed-form expressions for access success probability and system throughput are derived. Simulation results show that, compared with the traditional dynamic ACB-based NOMA random access algorithm (DNRA) and the fixed ACB-based orthogonal random-access algorithm (FORA), the proposed method improves system throughput by 30.41% and 48.22%, respectively. Furthermore, under varying user loads, the method consistently achieves higher access success probabilities and maintains high system throughput across different network conditions.

In existing blind estimation algorithms for frequency-hopping parameters based on time-frequency ridges, performance degrades significantly when the signal-to-noise ratio(SNR) falls below-5 dB. To address this issue, an improved blind estimation method based on time-frequency ridges is proposed. First, the time-frequency matrix of the received signal is obtained using the short-time Fourier transform (STFT). Then, Gaussian filtering is applied for signal smoothing and noise suppression. Since the energy distribution of fixed-frequency interference differs from that of frequency-hopping signals in the time-frequency matrix, an energy cancellation method is used to eliminate such interference. Next, OTSU thresholding is applied to the matrix after energy cancellation to further remove residual fixed-frequency interference and noise, yielding a clearer time-frequency representation. Time-frequency ridges are then extracted from this image, and the least-squares method is employed to fit the hopping instants along these ridges, enabling accurate estimation of frequency-hopping parameters. Simulation results show that the proposed method effectively produces clean time-frequency images even at SNR levels as low as-5 dB in the presence of fixed-frequency interference. The average relative estimation error of each parameter remains below 1%, and the method demonstrates strong robustness against varying intensity and quantity of interference.

With the emergence of wide-area Internet of things (IoT) applications, a novel cell-free hybrid non-terrestrial-terrestrial network is proposed to meet the need for seamless connectivity across vast geographic regions. The system leverages two distinct deployment modes of non-terrestrial network (NTN) devices to achieve global coverage, where the NTN devices function either as wireless access points or as wireless relays. The differences between these two deployment modes are analyzed, and appropriate deployment scenarios are discussed for each. A case study is conducted to evaluate the feasibility of the proposed system. The results indicate notable improvements in both energy efficiency and communication efficiency. Specifically, the proposed scheme achieves more than a sevenfold increase in both metrics. Finally, the potential opportunities, challenges, and future research directions of the system are discussed.

To address the limitation of single-mode motion in micro piezoelectric robots, this study proposes a piezoe-lectric-driven micro amphibious crawling robot. The robot utilizes the inverse piezoelectric effect of piezoelectric ceramics to excite multiple vibration modes of its body, enabling both terrestrial crawling via contact friction and aquatic swimming via jet flow, thus achieving amphibious locomotion. A compact structure driven by a single-phase excitation signal allows dual-mode motion on land and in water. Finite element modeling was conducted using Ansys 19.0 to analyze the robot's structural design, operating principle, and optimal working modes. A prototype was fabricated and experimentally tested. At a driving frequency of 17.0 kHz and a peak -to -peak voltage of 100 V, the robot achieved a maximum crawling speed of 130 mm/s, equivalent to 6.5 body lengths per second. At 227.0 kHz and the same voltage, its average swimming speed on the water surface reached 50 mm/s, or 2.5 body lengths per second. The consistency between simulation and experimental results verifies the feasibility of the proposed structural design and confirms the validity of the operating principle.

Low-frequency road noise is a primary contributor to in-cabin noise and directly affects overall vehicle comfort. With the rapid development of electric vehicles, this issue has attracted increasing attention from the automotive industry. This study conducts an experimental investigation on the low-frequency noise contribution in a new energy multi-purpose vehicle(MPV) using the classical transfer path analysis (TPA) method. By integrating modal analysis and operational deflection shape (ODS) analysis, the noise-contributing components along the transmission path are identified. For the identified components, structural optimization is performed through simulation, including tailgate reinforcement and rigidity enhancement, aiming to increase modal frequencies and decouple problematic resonance frequencies. The effectiveness of the optimization is validated through vehicle testing. Test results indicate that the main contributing frequencies are 27 Hz and 43 Hz. The 27 Hz component arises from the X-direction rigid body mode of the rear suspension and the first-order rigid swing mode of the tailgate. After optimizing the tailgate structure, its modal frequency increased from 29.2 Hz to 32.9 Hz, resulting in a 4 dB(A) noise reduction in the front row and 6.4 dB(A) in the rear row. The 43 Hz component is mainly due to the first-order mode of the panoramic sunroof. By modifying its surface curvature, the modal frequency decreased from 37.8 Hz to 32.1 Hz, leading to noise reductions of 7 dB(A) in the front and 3 dB(A) in the rear. The proposed optimization strategies significantly improve interior noise performance and provide valuable references for low-frequency road noise control in vehicle design.

With the acceleration of urbanization and the effects of global climate change, urban flooding has become increasingly frequent. Based on an extensive literature review, a vulnerability index system comprising seventeen indicators was constructed to assess urban flooding vulnerability in thirteen prefecture-level cities in Jiangsu Province. The combination weighting method based on game theory was applied to determine the vulnerability levels. The results indicate that torrential rainfall frequency, value added of the tertiary industry, GDP per unit land area, population density, urbanization rate, impervious surface ratio, and per capita road area are key indicators in evaluating vulnerability. Rapid economic development coupled with relatively outdated urban planning concepts are major factors contributing to urban flooding vulnerability. The southern region of Jiangsu is the most vulnerable, followed by the central and northern regions. The study further proposes targeted strategies to help mitigate urban flood risks and enhance resilience in vulnerable regions.

The middle and lower reaches of the Yangtze River are among China's most economically developed regions, characterized by abundant precipitation and a steady water flow. However, a serious imbalance exists between water resource availability and regional economic development. Nantong City, located on the northern bank of the Yangtze River estuary, exemplifies this issue, facing persistent surface water shortages and heavy reliance on ground-water extraction. To support more rational and scientific water use, this study assesses the sustainability of water resources in Nantong using the ecological footprint method from 2012 to 2021. The results indicate that the overall ecological footprint of water resources in Nantong has remained relatively stable, yet significant disparities exist across districts, primarily due to variations in industrial structure. The ecological carrying capacity and water resource load index fluctuate substantially, with consistent trends across regions that correlate positively or negatively with average precipitation. This suggests that increased rainfall improves ecological carrying capacity while easing water stress. Additionally, the water resource ecological footprint per 10 000 yuan of GDP shows a clear downward trend, reflecting improving efficiency in water use. However, Nantong has remained in an ecological water deficit over the long term, highlighting the need for effective measures to reverse this trend.