Yufei Zhang

Reliable recognition of unmanned aerial vehicle (UAV) communication signals is essential for low-altitude airspace safety and UAV monitoring. In practical electromagnetic environments, UAV signals exhibit complex time-frequency characteristics, and unknown signal types frequently appear, making open-set recognition necessary. This paper proposes a geometry-energy open-set recognition (GE-OSR) method for UAV signal identification. First, a time-frequency convolutional hybrid network is developed to learn multi-scale representations from raw UAV signals. Then, learnable class embeddings with a dual-constraint embedding loss are introduced to improve feature compactness and separability. In addition, a free-energy alignment loss is introduced to assign low energy to known signals and high energy to unknown ones, forming an adaptive rejection boundary. Experiments under different signal-to-noise ratios (SNRs) and openness levels show that GE-OSR provides stable performance. At 0 dB SNR under high openness, the method improves OSCR by about 2.95% over the recent S3R model and more than 6% over other baselines. These results show that GE-OSR is effective for practical UAV signal identification and unknown signal detection in complex low-altitude environments.

Reliable recognition of unmanned aerial vehicle (UAV) communication signals is important for low-altitude airspace safety and UAV monitoring systems. In real electromagnetic environments, UAV signals often show complex time-frequency patterns, and many unknown or new signal types can appear. These problems make open-set recognition necessary for practical UAV applications. In this paper, we propose a geometry-energy open-set recognition (GE-OSR) method designed for UAV signal monitoring. First, a time-frequency convolutional hybrid network is developed to learn multi-scale representations from raw UAV communication signals. Then, learnable class embeddings are added with a dual-constraint embedding loss to make the feature space more compact and separable. In addition, a free-energy alignment loss is introduced to guide the model to assign low energy to known UAV signals and high energy to unknown ones, which helps build an adaptive rejection boundary. Experiments under different SNRs and openness levels show that GE-OSR provides stable performance. At 0 dB SNR under high openness, the method improves OSCR by about 2.5% over the recent S3R model and more than 6% over other baselines. These results show that GE-OSR is effective for practical UAV signal identification and unknown signal detection in complex low-altitude environments.

The rapid development of Multi-access Edge Computing (MEC) technology is transforming traditional platform services and enabling realization of the mMTC vision of 5G networks. MEC allows computational tasks to be offloaded to devices at the network edge, enabling real-time crowd sensing. However, due to the limited resources in MEC environments, it is essential to propose efficient service deployment and traffic management strategies that balance Quality of Service (QoS) with costs. This paper addresses the challenge by modeling the QoS-effective joint service deployment and traffic management problem (QST) as a nonlinear integer optimization problem. We propose a customized genetic algorithm called GA4QST, which aims to minimize cost-performance ratios. In the experimental section, GA4QST is compared with baseline algorithms in terms of efficiency and effectiveness. Although GA4QST exhibits slightly increased complexity compared to the original genetic algorithm, it performs exceptionally well in balancing benefits and average time costs. GA4QST demonstrates strong capabilities in finding optimal solutions, consistently outperforming baseline algorithms. This further confirms the effectiveness and potential applicability of GA4QST in real-world scenarios. Finally, we also explore the impact on optimization outcomes of system configurations such as service diversity, data volume, processing power, and network characteristics. The results indicate that GA4QST has broad applicability and represents a feasible solution for practical MEC applications.

Biologically inspired product design (BIPD) inherently encompasses the concept of sustainability. It acquires inspiration from natural organisms, and the references in aspects such as form, structure, and function typically contribute to efficient resource utilization and environmentally friendly coexistence. However, past studies have mainly evaluated from the perspective of designers and researchers, which is relatively subjective. It is difficult to meet the real needs of industry and market. At the same time, the method of establishing indicators is not scientific enough, and the importance of indicators is not ranked. This research integrates the concept of sustainable design into the BIPD evaluation system, comprehensively considering the evaluation indices of different stakeholders such as sustainable designers, industrial designers, and users and decision-makers of design companies. By employing the analytic hierarchy process, a complete and systematic evaluation index model is constructed. This model can comprehensively and accurately screen and evaluate design proposals during the conceptual design stage of BIPD. Through this approach, it effectively averts resource waste caused by incorrect decisions in the production process, optimizes resource allocation, meets user requirements and vigorously promotes the sustainable development of BIPD throughout its entire life cycle.

With the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collection methods face limitations. UAVs have emerged as a promising solution for overcoming these challenges by facilitating data collection and transmission in various environments. However, existing UAV trajectory optimization algorithms often overlook the critical factor of the battery capacity, leading to potential mission failures or safety risks. In this paper, we propose a trajectory planning approach Hyperion that incorporates charging considerations and employs a greedy strategy for decision-making to optimize the trajectory length and energy consumption. By ensuring the UAV’s ability to return to the charging station after data collection, our method enhances task reliability and UAV adaptability in complex environments.