Communications engineering最新文献

Rapid and accurate simulations of fluid dynamics around complicated geometric bodies are critical in a variety of engineering and scientific applications. While scientific machine learning (SciML) has shown considerable promise, most studies in this field are limited to simple geometries. This paper addresses this gap by benchmarking diverse SciML models, including neural operators and vision transformer-based foundation models, for fluid flow prediction over intricate geometries. We evaluate the impact of geometric representations-Signed Distance Fields (SDF) and binary masks-on model accuracy, scalability, and generalization using a high-fidelity dataset of steady-state flow over complex geometries. We introduce a unified scoring framework that integrates metrics for global accuracy, boundary layer fidelity, and physical consistency. Our findings reveal that newer foundation models significantly outperform neural operators, particularly in data-limited scenarios. In addition, binary mask representation enhances the performance of vision transformer models by up to 10%, while SDF representations improve neural operator performance by up to 7%. Despite these promises, all models struggle with out-of-distribution generalization, highlighting a critical challenge for future SciML applications. Our work paves the way for robust and scalable ML solutions for fluid dynamics across complex geometries.

Despite the rapid development of autonomous driving, drivers still need to take over when autonomous driving exceeds its design scope or malfunctions. The role of drivers is undergoing a significant transformation from operators to backup users. The existing human driving behaviour models focus more on the behaviour of humans as operators, with static and time-invariant characteristics. However, as backup users, human behaviour characteristics during the takeover process exhibit dynamic and time-varying characteristics, and traditional driver models can no longer describe these, making it difficult to support the safe development of autonomous driving. Unfortunately, the evolution mechanism of driver behaviour is unclear, which has led to the continuous occurrence of accidents in autonomous vehicles. To support the safe development of autonomous driving, we studied the changes in drivers' cognition, decision-making, and control behaviours during the takeover, revealing the evolution mechanism of driver behaviours during the takeover. On this basis, a progressive reshaping model of human driving behaviours is constructed. The comparison with actual driver control data shows that the accuracy of the proposed model is 88.57%, providing a new perspective for understanding driver behaviour during emergency takeover and having certain application value in the research of autonomous driving technology.

Iontronics combines ions as information carriers with electronic-like operations, enabling the creation of ion-based integrated circuits that offer unique signal processing, chemical regulation, and enhanced bio-integrability. Existing simulation tools encounter difficulties in effectively modeling integrated iontronic components, highlighting the need for specialized design and simulation methodologies. Here we present a design approach toward ion-based large-scale integrated circuits, inspired by electronic integrated circuit abstraction levels. We develop a compact model for the iontronic bipolar diode, with a conceptual framework applicable to other iontronic components. The model is implemented using standard Electronic Design Automation tools, allowing simulation of static and dynamic properties of iontronic circuits. Simulated results match measurements from fabricated small-scale iontronic circuits. The proposed simulation approach employs Monte Carlo methodology and enables exploration of how component non-uniformity influences circuit behavior. We demonstrate the model's utility by simulating ion-based integrated circuits, including an iontronic decoder and diode bridge. Expanding traditional circuit design tools to support iontronics could advance the development of hybrid systems that leverage both electronic and ionic functionalities.

Open Radio Access Networks (O-RAN) offer a flexible RAN architecture for future 6G systems, yet their complexity and lack of real-world testbeds pose interoperability challenges, particularly with emerging software platforms and robotic systems. Here we present a real-world software-defined radio testbed based on an open-source 4G long-term evolution (LTE) system, integrated with the near-real-time (Near-RT) RAN Intelligent Controller (RIC) via standard O-RAN E2 interfaces. It enables connectivity with robotic end devices such as a haptic controller and robotic arm, demonstrating the activation of E2 functionality within a live RAN environment. The testbed enables haptic operation with sub-one-second latency and block error rate (BLER) under 12% for tasks such as dental inspection use cases. We also demonstrate replacement of software-defined radios (SDRs) with low-power mobile dongles, achieving comparable 10 Mbps throughput while cutting power consumption by 90%. This setup establishes a foundation for advancing research and integration in managing next-generation RANs.

Engineering the properties of electromagnetic wavefronts has become essential to imaging, wireless security, sensing, and wireless communication. In particular, wavefronts that exhibit low spatial coherence can enable sensing functionalities with high accuracy and low latency. The typical use of such wavefronts cannot take advantage of these possibilities, as they require the ability to dynamically reconfigure the wavefront in a controllable and repeatable fashion, over a broad spectral bandwidth. Here, we propose a new approach for generating broadband reconfigurable wavefronts which not only exhibit low spatial coherence at a particular frequency, but are also decorrelated with the wavefronts simultaneously generated at other frequencies. We demonstrate that this frequency-domain decorrelation is a key feature that, in combination with dynamic reconfigurability, enables localization measurements with an order-of-magnitude improvement in accuracy compared to the state of the art.

Birds morph the shape of their wings during flight to achieve impressive maneuverability and adapt to dynamic environments, such as cities and forests. Engineers have explored using avian-inspired designs with feather-based wing morphing to achieve similar capabilities with small uncrewed aircraft. However, engineered feather designs haven't incorporated the microscopic structural features that prevent feather separation for natural fliers within dynamic airflows and during wing shape changes. Without a fastening mechanism, gaps can form throughout the wing's surface that impair maneuverability and shorten flight range. Here we show how electrostatic feather fastening adapts aerodynamic force generation to improve maneuverability and efficiency. Further, the electrostatically adhered feathers offered a preferable relationship with velocity, improving on passive feather aerodynamics and often generating responses comparable or favorable to the baseline engineered wing at higher flow speeds. As small aircraft are expected to fly faster, further, and with advanced aerobatic capability, feathered morphing wings incorporating electrostatic adhesion will advance aircraft adaptability for successful operation in complex environments.

Efficient heat transfer across contacting surfaces is essential for effective thermal management; however, it is often hindered by thermal contact resistance resulting from complex surface topography. Here we present an interpretability analysis utilizing deep learning based to predict and visualize the key features influencing thermal contact resistance. We developed a convolutional-neural-network-based model trained on an extensive dataset generated using surface fractal theory. The model's predictive performance was validated against experimental data, in which surface topography and thermal resistance were measured for ground and turned steel specimens. Our model accurately predicts thermal contact resistance and estimates the actual contact area. Moreover, interpretability and visualization techniques reveal that both direct contact spots and non-contact regions of the surface topography significantly affect heat transfer, surpassing the explanatory power of traditional roughness parameters. This approach provides a robust methodology to enhance the fundamental understanding and predictive capabilities regarding thermal contact resistance.

Millimeter-wave switches are essential for reconfigurable and adaptive communication systems, yet current solutions often face trade-offs between performance, scalability, and cost. Here we present a scalable, high performance and cost-effective approach to develop reconfigurable millimeter-wave substrate integrated waveguide (SIW) devices by integrating vanadium dioxide (VO₂) thin films with printed circuit board (PCB) technologies. The integration technique involves depositing VO₂ films on thin, flexible polymer substrates, which are then transferred and affixed to PCB circuits. The VO₂ is thermally activated and selectively doped to reduce power consumption depending on applications. Using experimental prototypes, we demonstrate several reconfigurable devices operating in the millimeter-wave band, including series and parallel switches and a reconfigurable hybrid coupler that transforms into dual through-line SIWs. Electromagnetic simulations and measurements validate the approach, revealing low insertion loss, good isolation, and broadband operation. This method simplifies fabrication and supports large-area integration, offering a practical route to scalable, low-cost, reconfigurable millimeter-wave components.

Disaster loss estimations are valuable risk reduction tools but rarely consider the loss trade-offs when a building stock is subjected to multi-hazard impacts. Here, we developed an approach to simulate direct economic losses of a housing stock and explore loss reduction across scenarios of housing typology distributions. We used multi-objective optimisation to model wind and seismic losses in Itbayat, Batanes, Philippines. Using Monte Carlo simulation, 11,628 housing stock scenarios were modelled under two cases of paired extreme hazard intensity thresholds, identifying Pareto optimal solutions that were further analysed against a socio-technical framework. We show that the current housing stock distribution can sustain lower multi-hazard losses by achieving more optimal combinations of lightweight and reinforced concrete typologies. However, transitioning to this desired stock distribution becomes a trade-off of not just wind-seismic loss reductions but also of socio-technical considerations such as households' risk perceptions. Our study advances risk reduction strategies by streamlining loss estimations to inform collective and safer multi-hazard construction practices.

Developing photovoltaic tracker algorithms for bifacial solar modules in agrivoltaic systems requires computationally intensive raytracing simulations to accurately quantify irradiation. Sunlight distribution on ground and module levels is essential for optimizing the setup and operation of tiltable PV systems, maximizing crop and electrical yield under various weather conditions and tilt configurations. We introduce a deep learning-based surrogate model that computes ground-level irradiation in a complex agrivoltaic scene with PV tracking. The surrogate model is physics-informed since the training data includes raytracing outputs based on real weather data. It computes the ground irradiance map based on direct normal irradiance, diffuse horizontal irradiance, solar position, and system geometry in just 3ms, four orders of magnitude faster than standard raytracing. The presented encoding of the 3D scene allows the calculation of ground irradiance using generative regression models. Our surrogate model allows on-the-fly raytracing calculations for edge computing-based PV tracker applications, where computational efforts must be minimized to enable efficient management and optimization of PV systems.