Communications engineering最新文献

Solar radiation heterogeneity across exposed heritage sites drives material deterioration, yet systematic quantification remains elusive due to the absence of operational assessment frameworks. Here, we present a multidisciplinary framework that integrates high-resolution geometric data with multidimensional sky models to quantify solar radiation distribution of large-scale heritage surfaces. Applied to grotto temple sites, we demonstrate that annual cumulative direct solar radiation exhibits substantially greater spatial variability across surface orientations than diffuse radiation. Monthly direct radiation shows marked inter-heritage variations with less predictable patterns compared to diffuse components. Radiation intensity differences between winter and summer solstices are pronounced, with noon-time exposure varying up to threefold across orientations. Critically, solar radiation-driven thermal stress calculations reveal extreme gradients, with protruding surfaces and edges experiencing pressures of 400-500 kPa, while adjacent shaded areas sustain only 50-100 kPa, creating more than fivefold stress differentials. This flexible framework provides site-specific performance metrics to inform the development of advanced conservation materials and targeted intervention strategies for diverse heritage sites.

The crack healing capacity of self-healing concrete is crucial for enhancing structural durability, especially in aggressive environments where the dynamic progression of healing depth directly influences service life. This study introduces a modeling and prediction approach based on Polynomial Chaos Expansion (PCE) to quantitatively assess the crack cross-sectional repair rate throughout the full healing cycle. A foundational database is first established by statistically identifying key factors governing healing behavior. A first-order PCE surrogate model is developed to characterize the temporal evolution from early-stage variability to nonlinear saturation. Dimensionality reduction combined with order elevation enhances accuracy under limited data conditions. To overcome the constraints of conventional Hermite polynomials bound by Gaussian assumptions, a generalized PCE framework accommodating arbitrary distributions is formulated, enabling broad applicability across healing scenarios. Extrapolative validation on unmodeled healing ages confirms the model's robustness and reliability throughout all healing stages. This work provides a reliable quantitative framework for predicting service life and optimizing repair strategies in engineering practice.

Multiport microwave power splitters are key building blocks in high-frequency systems such as phased arrays, beamforming networks and measurement setups, but are usually designed using fixed circuit topologies that are difficult to adapt to many-port or unconventional layouts. This paper introduces a scalable inverse-design framework for multiport microwave power splitters that is directly compatible with three-dimensional printing. Here we combine gradient-based optimization with adjoint electromagnetic simulations to automatically shape a dielectric device that meets specified waveform targets at multiple output ports. The method is demonstrated on a four-port power splitter operating at ten gigahertz, fabricated using a polymer powder bed fusion process (multi jet fusion) with simple constraints on minimum feature size and material permittivity. Numerical simulations and waveguide measurements show close agreement in transmission, reflection, and port-to-port balance, indicating robust performance despite manufacturing tolerances. The approach is topology-agnostic and fabrication-aware, enabling economical prototypes and systematic scaling to devices with many ports. This work establishes a general route for integrating inverse design and three-dimensional printing in microwave engineering, and could be extended to other radio-frequency and millimetre-wave components.

Magnetic resonance imaging-guided acoustic trapping is expected to manipulate drug carriers (e.g., microbubbles) within the body, potentially improving carrier concentration at tumor sites and thereby enhancing targeted therapy outcomes. However, accurate trap generation remains challenging due to complex wave propagation through multiple tissue materials. Moreover, respiration-induced tissue motion imposes stringent requirements on computational efficiency for rapid phase updates. Here we propose a machine learning-based model and a closed-loop control scheme to modulate phase patterns rapidly. The model delivers precise time-of-flight prediction (mean err. ≤ 0.24 μs) within 26 ms for 196 transducer elements. In proof-of-concept experiments, computer vision feedback permits fast (about 15 frames per second) position adjustment of a trapped polystyrene ball (Ø2.7 mm). This control scheme helps lessen the ball's spatial drift induced by time-varying multi-medium environments. These experiments on robotic manipulation support our model's potential for future magnetic resonance imaging-guided targeted therapy.

The generation of acoustic vortexes sparks intense research interest since they have applications in modern wave-based technologies, such as underwater communication and particle manipulation. However, the existing schemes mainly rely on a phase mask to excite a single vortex beam, thereby lacking the functionality and adaptability for practical scenarios. In this article, we propose a feasible methodology to realize multi-channel ultrasonic Bessel vortex beams at megahertz. By leveraging the concept of spatial multiplexing, the adjacent pixel of the metalens can be assigned to independently generate non-diffraction ultrasonic vortices with different topological charge and spatial orientation, without losing the characteristics of the helicoidal wavefront. We experimentally designed a four-channel metalens with a high fabrication accuracy of 0.2 mm pixel size and measured the far-field ultrasound distribution in the water. Both topological charge and radiation direction of the generated vortices can be precisely controlled as predicted, showcasing great agreement with simulation results with a directional error of less than 1°. Moreover, the intensity of the vortex can be tuned by gradually combining multiple channels into one. The proposed scheme enhances the flexibility of manipulating ultrasonic vortex and offers more possibilities in designing multi-functional ultrasound devices.

Physical inactivity is the fourth largest cause of global mortality. Health organizations have requested a tool to objectively measure physical activity because many specific and causal relationships between activity and health outcomes are not clearly understood. Existing activity monitors are either unsuitable for large-scale use or have substantial error. We present OpenMetabolics, a biomechanically-informed activity monitor that employs a smartphone in a pants pocket which measures leg motion to estimate energy expenditure. OpenMetabolics uses a data-driven machine learning model to capture the relationship between underlying leg muscle activity and energy expended during common physical activities. OpenMetabolics estimated energy expenditure with 18% cumulative error across all real-world activities, approximately two times lower than existing tools. We developed a pocket motion artifact correction model to accurately monitor energy expenditure when the smartphone is in a pocket of various types of clothing. A week-long, at-home monitoring study highlighted individual and population-level activity patterns across various timescales. We have made the data, code, and smartphone application open source. This accurate and accessible activity monitor could be deployed for large-scale studies with many patient populations to relate activity to health outcomes, inform health policy, and develop interventions.

Recent advances in microelectromechanical systems (MEMS) have advanced inertial sensor technology. For resonant gyroscopes, sensitivity scales with the maximum velocity of the resonating mass, as higher velocities amplify the Coriolis force for faster and more accurate inertial signal detection-critical in navigation applications. Conventional MEMS remain in linear regimes, with velocities typically below 5 m/s. A recent Defense Advanced Research Projects Agency (DARPA) initiative challenges researchers to push resonator speeds toward material fracture limits, targeting up to 200 m/s and exploring regimes dominated by strong nonlinearities. This work investigates velocity limits in piezoelectrically driven mechanical resonators imposed by nonlinear dynamics and material constraints. We experimentally demonstrate an AlN bimorph wedge resonator reaching 50 m/s, achieving a ten-fold improvement over current limits. These results highlight the feasibility of operating MEMS devices at much higher velocities, paving the way for next-generation inertial sensors with increased performance. The resonator operates at a higher-order mode near 1.81 MHz, with clear evidence of Duffing-type nonlinearities at large drive amplitudes, as confirmed in time-domain and frequency-domain measurements.

Speckle-pattern interrogation offers a route to high-resolution spectral sensing, but its uptake has been constrained by poor temporal stability under real-world conditions. Here, we introduce an ultra-stable speckle-based architecture that overcomes these limitations and enables real-time structural health monitoring of uncrewed aerial vehicles. Unlike conventional approaches that rely on large-scale, free-space passive speckle decorrelation, our system utilizes an ultra-compact speckle pattern via laser-written scattering centers in a high aspect ratio flat fiber, encapsulated within a 3D-printed polylactide housing. This architecture suppresses environmental drift and enables robust, high-fidelity interrogation of fiber Bragg gratings in dynamic aerospace conditions. The system demonstrated exceptional stability under sustained mechanical excitation, maintaining measurement integrity at ±7 G sinusoidal acceleration along the axial direction. Furthermore, in-flight validation across uncrewed aerial vehicle flight tests confirmed real-time strain interrogation in the -100-400 µε range with a standard deviation in measurement of 1.63 µε. These results mark the demonstration of stable, real-time speckle-based interrogation in flight, establishing a path toward broader deployment of specklemeters in harsh environments.

Targeted communication is made possible using beamforming. It is extensively employed in many disciplines involving electromagnetic waves, including arrayed ultrasonic, optical, and high-speed wireless communication. Conventional beam steering often requires the addition of separate active amplitude and phase control units after each radiating element. The high-power consumption and complexity of large-scale phased arrays can be overcome by reducing the number of active controllers, pushing beamforming into satellite communications and deep space exploration. To address this, we propose a phased array antenna design based on dimensionality-reduced cascaded angle offset phased array (DRCAO-PAA). By applying singular value decomposition (SVD) to compress the coefficient matrix of phase shifts, our method reduces the number of active controllers while maintaining beam-steering performance. Furthermore, the suggested DRCAO-PAA was sing the singular value deposition concept. For practical application the particle swarm optimization algorithm and deep neural network Transformer were adopted. Based on this theoretical framework, an experimental board was built to verify the theory. Finally, the 16/8/4 -array beam steering was demonstrated by using 4/3/2 active controllers, respectively.

Neuronal networks have driven advances in artificial intelligence, while molecular networks can provide powerful frameworks for energy-efficient information processing. Inspired by biological principles, we present a computational framework for mapping synthetic gene circuits into bio-inspired electronic architectures. In particular, we developed logarithmic Analog-to-Digital Converter (ADC), operating in current mode with a logarithmic encoding scheme, compresses an 80 dB dynamic range into three bits while consuming less than 1 µW, occupying only 0.02 mm², and operating at 4 kHz. Our bio-inspired approach achieves linear scaling of power, unlike conventional linear ADCs where power consumption increases exponentially with bit resolution, significantly improving efficiency in resource-constrained settings. Through a computational trade-off analysis, we demonstrate that logarithmic encoding maximizes spatial resource efficiency among power consumption and computational accuracy. By leveraging synthetic gene circuits as a model for efficient computation, this study provides a platform for the convergence of synthetic biology and bio-inspired electronic design.