Communications engineering最新文献

Extremely low frequency (ELF, 3-30 Hz) signals possess strong cross-medium communication capabilities, making them particularly well-suited for underground and underwater environments. However, traditional low-frequency (LF) transmission systems are large and inefficient, posing significant limitations in practical applications. In recent studies, mechanical antennas have been explored to generate LF signals, but current approaches rely on bulky equipment with limited range, making them unsuitable for personal use or integration into small unmanned devices. To address this challenge, this study introduces a flexible, magnet-based miniaturized LF mechanical antenna, fabricated using 3D printing. The antenna consists of a macro-fiber composite layer and a flexible permanent magnet film, and features an extremely compact volume (<6.8 cm³) and low weight (<50 g). It is also highly flexible, allowing for easy integration into diverse applications. Its transmitted signal can reach 60 m before the magnetic field strength attenuates to 1 pT. Mounted on an unmanned aerial vehicle (UAV), the antenna facilitates reliable communication between quadruped robots operating outside caves and aerial robots located deep within cave interiors, where high-frequency (HF) signals cannot penetrate. This study demonstrates robust LF cross-medium communication between UAV and ground robots in cave environments, paving the way for unmanned collaboration in scenarios inaccessible to HF wireless signals.

Stress concentrations at geometric irregularities such as reentrant corners make it challenging to efficiently simulate localized plastic deformation in engineering materials. Fully nonlinear models capture these effects accurately but are computationally costly, whereas simplified elastic analyses neglect important nonlinearities. Here, we present NeuberNet, a Multi-Task Nonlinear Manifold Decoder that learns mappings between far-field displacement boundary conditions from low-fidelity elastic simulations and the corresponding high-resolution stress and strain fields derived from elastic-plastic axisymmetric solid mechanics, under assumptions of small-scale plasticity and bilinear isotropic hardening. NeuberNet serves as a data-driven implementation of the substructuring principle, designed to model complex geometries by activating plastic behavior only near stress raisers where nonlinearities arise. We provide guidelines for mesh resolution in low-fidelity simulations, demonstrate NeuberNet's ability to identify violations of the small-scale plasticity assumption, and assess its robustness to nonlinear hardening laws. We also show that NeuberNet generalizes to 3D problems with axisymmetric geometries and non-symmetric boundary conditions. Overall, NeuberNet provides a reliable and computationally efficient framework for small-scale plasticity analysis.

This work investigates the thermal stability of Cu-Cu bonding using a thin Ag passivation layer in applications targeting advanced packaging. Conventional Cu-Cu bonding often requires elevated temperatures (≥250 °C) that can exacerbate thermal stress and limit process flexibility, making multi-chip stacking more challenging. By introducing a 3 nm Ag passivation layer, we demonstrate reliable bonding at lower temperatures with improved durability against high-humidity and high-temperature environments, as confirmed by both Highly Accelerated Stress Tests (HAST) and burn-in measurements. In-situ transmission electron microscopy (TEM) and 4D-STEM strain mapping reveal that Ag diffusion along Cu grain boundaries not only retards abnormal grain growth but also reduces interfacial void formation at elevated temperatures. These enhancements collectively maintain a stable interface and superior mechanical strength relative to that for non-passivated Cu-Cu bonding. The results highlight the importance of metal passivation in enabling low-temperature Cu-Cu bonding technologies with robust thermal stability, providing the feasibility for next-generation advanced packaging platforms.

Dynamic weighing systems are crucial for bridge safety, yet current weighing systems are either too costly for widespread urban use or unable to track vehicles reliably in dense traffic. Here we present a video-strain inverse estimation system that combines computer vision with structural strain sensing to measure dynamic vehicle loads on bridges. The method employs a lightweight deep learning detector to recognize vehicle features, a multi-object tracking model to capture trajectories and lane positions, and a developed analytical algorithm to estimate vehicle weight from measured strains. We validated the system with field data from a heavily trafficked urban bridge. The system achieves vehicle recognition with near-baseline accuracy while using only 17% of the original model parameters and running 1.72 times faster, identifies lanes with complete accuracy with a missed detection rate of just 0.56%, and estimates total vehicle weights within 2% error. This low-cost and reliable approach advances intelligent bridge monitoring and supports digital twins of critical urban infrastructure.

Roll-to-roll microcontact printing enables high-throughput production of flexible electronic devices by continuously transferring inks onto substrates via polydimethylsiloxane (PDMS) stamps. Traditional rectangular or cylindrical PDMS stamps yield uniform pattern sizes, limiting manufacturing versatility. This study introduces V-shaped PDMS stamps for variable pattern printing using a single stamp geometry. A physics-based deformation model was developed by combining finite element simulations and experiments to characterize the out-of-plane behavior of V-shaped PDMS under displacement. Leveraging this model, we implemented a neural network-based model predictive control system to precisely regulate vertical displacement and achieve desired pattern dimensions. Experimental results demonstrate that a single V-shaped PDMS stamp can reliably produce variable pattern sizes with high repeatability, improving the adaptability and process efficiency of roll-to-roll microcontact printing for flexible electronics manufacturing.

Micro- and nano-electromechanical systems (M/NEMS) are in high demand for cutting-edge future technological solutions. Their strongly nonlinear nature is regarded as beneficial for optimising performance metrics for a wide range of engineering applications. As model-free experimentation remains limited to forward time observations, important dynamic features can be left uncharted, resulting in an incomplete dynamical landscape. Here we address this issue by employing experimental continuation - a model-free technique for constructing bifurcation diagrams by tracking steady-states and/or periodic responses directly in a physical experiment. This approach is unexplored for investigating micro-scale systems with fast timescales. Our state-of-the-art experiment investigates an active MEMS cantilever operated as a self-oscillator with a natural frequency of 100 kHz; the fastest timescales of a mechanical system probed with experimental continuation. We explore the cantilever's nonlinear responses to external, periodic excitation via a sequence of one-parameter response curves. By experimentally mapping out the MEMS cantilever's stable and unstable periodic orbits, we expose the dynamic landscape by rendering a multi-valued response surface across a range of forcing frequencies and amplitudes. An atypical, non-Duffing-like bifurcation structure is revealed.

The treatment of dairy wastewater (DW), characterized by high organic load and lipid/protein content, remains challenging due to the energy-intensive nature of aerobic processes and instability of anaerobic methods. This study developed a self-regulating two-phase anaerobic digestion (TPAD) system integrating an anaerobic baffled reactor (ABR) with an up-flow anaerobic sludge blanket (UASB) reactor. Sequential phase separation in the ABR enables microbial self-organization for staged lipid adsorption, protein denaturation, and hydrolysis-acidification, ensuring stable UASB input. Laboratory-scale operation achieved exceptional chemical oxygen demand (COD) removal (97.06-99.01%). Full-scale implementations across three Chinese provinces demonstrated robust performance, with COD removal of 78.13-93.46%, high methane content (83.20-83.94%), sludge reduction >75.00%, and reductions in energy consumption (64.71-85.03%) and greenhouse gas emissions (88.01-97.09%) compared to conventional systems. Microbial analysis confirmed functional spatial divergence. The TPAD system presents a regionally-proven, versatile, and scalable solution to transform DW management from a disposal cost into a biogas-generating process.

Cold atmospheric pressure plasma (CAP) is emerging as a clinically relevant therapy for dermatological conditions such as actinic keratosis, warts, and chronic wounds. However, these therapies lack strategies to monitor CAP delivery in situ and to ensure delivery of an effective CAP dose without unwanted toxicity. CAP acts as a therapeutic agent in these biomedical applications primarily (but not solely) through reactive oxygen and nitrogen species (RONS) generated at transiently high local concentrations. Here we demonstrate the use of bio-electrochemical sensors capable of real-time measurements of key CAP RONS: hydrogen peroxide and oxidation-reduction-potential (ORP). In in vitro scratch assays and in vivo murine wound models, we used these sensors to establish dose-response relationships that link CAP exposure with wound (scratch) closure dynamics, cell proliferation, oxidative stress response, and scar reduction. Our results demonstrate that CAP treatment can be continuously monitored and actively controlled in situ, providing a framework for precision plasma medicine and safer, more effective clinical translation of CAP.

Offshore wind energy plays a vital role in addressing global energy challenges. Its true value emerges when integrated into holistic systems combining offshore wind farms with coastal power plants, energy storage, and marine ranches. Using East China as a case study, we develop and optimize such clusters to reduce construction and operation costs. Our results show that these clusters significantly enhance energy storage utilization, increase offshore wind absorption such that approximately 20% of wind farms achieve an annual generation absorption rate exceeding 95%, and improve frequency regulation of large power units, achieving an optimized storage configuration of 0.67 GWh. By employing fish cages as flexible loads, the regional absorption rate rises above 98%, generating economic benefits of 6.82 billion RMB annually. Marine ranches also provide 35 kilotons of high-quality protein, advancing food security. These findings highlight the transformative potential of integrating offshore wind into dynamic systems, redefining the interplay between renewable generation, storage, and ancillary services for the East China case. By unlocking the combined benefits of energy and marine resource synergies, this work lays the groundwork for sustainable energy innovation and sustainable development. Its applicability can extend to other regions, provided that local policies and biophysical conditions are met.

Rock thin-section image analysis is a fundamental task in geological and mineralogical research. Traditional methods rely on visual inspection by experts using optical microscopes, which are inherently subjective, experience-dependent, and time-consuming. Here, we introduce RoImAI, a vision foundation model specifically designed for rock thin-section microscopy images, enabling rapid and precise rock segmentation, identification, and lithology report generation. A large-scale dataset of rock thin-section microscopy images comprising 30,336 images and approximately two million rock particles from 17 different regions was constructed to develop and validate RoImAI. RoImAI leverages Transformer-based deep learning techniques to achieve high-precision segmentation across multi-center datasets from diverse geological regions. RoImAI employs a hierarchical classification strategy to identify rock particles accurately. Furthermore, RoImAI outperforms human experts in efficiency and accuracy when generating structured lithology reports. The intelligent analytical capabilities and high accuracy of RoImAI strongly enable the automated processing of the rapidly expanding volume of rock thin-section images.