Communications engineering最新文献

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.

The proliferation of distributed energy resources introduces multi-source uncertainties, including implicit uncertainties arising from third-party operators' partial observability of security constraints, challenging traditional distribution network planning methods dependent on model simplification and predefined scenarios. We address this gap via an adaptive hierarchical learning architecture that co-optimizes distributed energy resources location, capacity, and operational strategies data-drivenly, enabling autonomous learning of implicit constraints without full model knowledge. Our framework embeds a bi-level Stackelberg structure where Monte Carlo Tree Search autonomously generates planning schemes at the upper level, while multi-agent reinforcement learning directly learns operational policies from real-time data at the lower level under partial observability. Validation on both benchmark and large-scale practical distribution systems shows lower investment costs and faster solutions while maintaining voltage stability, demonstrating superior scalability and adaptiveness to implicit uncertainties versus scenario-based methods.

Intelligent metasurfaces, capable of shaping the electromagnetic field, have been extensively investigated in diverse scenarios, including beamforming, wireless communication, and electromagnetic imaging. Adaptable metasurface control is essential for their applications in practical communications engineering. Here we present a cyber-managed metasurface system to enhance the convenience of metasurface sub-array management, which integrates radio frequency energy harvesting with star-topology hybrid networks. By employing digitized phase-shifted transmission lines as tunable elements, the system not only enables electromagnetic manipulation and sensing capabilities but also achieves ultra-low power consumption. Each metasurface sub-array consists of 2 × 2 units, serving as a network node for data transmission and the sharing of harvested energy. Additionally, these metasurface sub-arrays, designed to resemble LEGO blocks, can be combined into various configurations, enabling flexible electromagnetic manipulation. The cyber-managed metasurface can be seamlessly integrated into wireless communication systems and passive wireless sensing networks, thereby providing versatility across diverse applications.

Neutron Resonance Transmission Analysis (NRTA) is a highly sensitive, non-destructive technique for nuclear material characterisation, but its application has been limited by its reliance on large, fixed, and costly installations. Here, we present a compact mobile NRTA system utilising a small ²⁵²Cf spontaneous neutron source, designated as a prototype "table-top NRTA system", to analyse nuclear materials, offering a mobile and cost-effective alternative to accelerators or deuterium-tritium generators. The pilot system, measuring 130 cm × 50 cm × 50 cm with a 42 cm flight path, enables time-of-flight measurements on nuclear material samples. The system's performance was demonstrated through NRTA measurements of simulated samples, including indium, hafnium, and cadmium metal plates. The experimental transmission spectra were compared with theoretical predictions using the PHITS Monte Carlo simulation and the JENDL-5 nuclear data library, enabling isotope identification below 5 eV. The obtained results underscore the system's potential as a complementary tool for nuclear security and safeguards verification, particularly in scenarios where access to large accelerator or reactor facilities is impractical, and where mobility, compactness, and cost-effectiveness are prioritised over throughput.