Communications engineering最新文献

Low-grade thermal gradients in marine environments represent an underexploited energy source for autonomous sensing and monitoring. Converting such small temperature differences into usable electrical power remains a key challenge for ocean-deployed systems. We present a deployable thermomagnetic generator thoroughly characterized for marine-relevant energy harvesting. The device powers an internet-connected sensor and harvests ultra-low temperature differences akin to those at the ocean surface. It draws heat from water and rejects it to ambient air, operating optimally at a temperature difference (ΔT) of ~7.5 °C. Laboratory prototypes generated up to 6.7 mW at ΔT ~ 10 °C with gentle airflow (~1 m s^-1). A separate controlled wave-tank demonstration validated stable operation and sensor powering under marine-like boundary conditions. Given its voltage and power margins, the generator could sustain multiple sensor nodes. Scalability and material assessments identify modular deployment and non-rare-earth alternatives as pathways toward practical marine energy harvesting and low-grade waste-heat recovery.

The primary challenge in active object tracking (AOT) lies in maintaining robust and accurate tracking performance in the complex physical scenarios. Existing end-to-end frameworks based on deep learning and reinforcement learning often struggle with high computational costs, data dependency, and limited generalization, hindering their performance in practical applications. Although embodied intelligence (EI) is promising to enable agents to learn from physical interactions, it cannot tackle severe anomalies happened in the complex scenarios. In order to address this issue, here we propose a novel embodied learning method, called the Cognitive Embodied Learning (CEL), which is inspired by the dual decision-making system of the human brain. The CEL can dynamically switch between normal tracking and anomaly handling modes, supported by specialized modules including the anomaly cognition module, the rule reasoning module, and the anomaly elimination module. Moreover, we further introduce the categorical objective function to address function non-measurability and data confusion caused by severe anomalies. Extensive unmanned aerial vehicle anomaly active target tracking experiments in both simulated and real-world scenarios demonstrate the superior performance of our method. Compared to the state-of-the-art methods, the CEL achieves a 361.4% increase in the success rate and a 54.4% improvement of the task completion efficiency, which highlights the potential of CEL to advance the field of AOT and open new avenues for more robust and intelligent tracking systems in the challenging environments.

Swarms of drones offer increased sensing aperture. When these swarms mimic natural behaviors, sampling is enhanced by adapting the aperture to local conditions. We demonstrate that this enables detection and tracking of heavily occluded targets. Object classification in conventional aerial images generalizes poorly due to occlusion randomness and is inefficient even under minimal occlusion. In contrast, anomaly detection applied to synthetic-aperture integral images remains robust in dense vegetation and independent of pre-trained classes. Our autonomous, centralized swarm searches for unknown or unexpected occurrences, tracking them while continuously adapting its sampling pattern to optimize local viewing conditions. We achieved average positional accuracies of 0.39 m with average precisions of 93.2% and average recalls of 95.9%. Here, adapted particle swarm optimization considers detection confidences and predicted target appearance. We present a new confidence metric that identifies the most abnormal targets and show that sensor noise can be effectively included in the synthetic aperture process, removing the need for costly optimization of high-dimensional parameter spaces. Finally, we provide a hardware-software framework enabling low-latency transmission and fast processing of video and telemetry data. Although our field experiments involved six drones, ongoing technological advances will soon enable larger, faster swarms for military and civil applications.

Free-space optical communication with high transmission bandwidth and small antenna size has been progressively deployed for ground-air-space communications in recent years. However, current mitigation methods, including adaptive optics and transmitter-side beam shaping, are often limited by design complexity and insufficient bandwidth. Here, we propose a receiver-side wavefront correction scheme based on optical pin beam-a ring-shaped, self-healing beam structure-formed via a static phase mask in front of the coupling lens. Unlike traditional optical pin beam methods requiring transmitter-side modulation, our design proactively reshapes the aberrated receiving beam into a stable optical pin beam with extended Rayleigh length, thereby improving mode matching and enhancing coupling resilience under turbulence. In a kilometer-scale outdoor experiment, we demonstrate a 100 Gbps free-space laser link, with coupled power stability increased by 26% and bit error rate decreased by up to 2 orders of magnitude compared with a Gaussian receiver. This receiver-side-only solution simplifies system architecture, ensures high-power compatibility, and offers a low-cost and scalable pathway for future optical ground stations, paving the way for ultra-long-distance, high-speed, and compact Free-space optical communication systems.

Efficient production of monoclonal antibodies (mAb) using Chinese Hamster Ovary (CHO) cells is central to pharmaceutical biomanufacturing. The clone selection process traditionally requires lengthy 7-to-14-day assessments to evaluate performance, which extends development timelines. Here we introduce a hybrid Luedeking-Piret Regression model that integrates mechanistic insights with machine learning to more accurately predict mAb yields in fed-batch CHO cultures. Using experimental data from the early growth stages (up to day 9) of seven (n=7) distinct CHO cultures, the model performed multi-step-ahead forecasting to predict final production. The model predicted monoclonal antibody titers on day 16 with a mean percentage error of 5.85%, correctly selected higher-performing clones in 76.2% of trials from leave-two-out cross-validation and accurately forecasted daily production trajectories from day 10 to day 16. The model's multi-step-ahead forecasting capabilities have the potential to accelerate clone selection, providing the biomanufacturing community with a computationally straightforward algorithm for predicting production yields.

With the continuous integration of advanced information and communication technologies into smart grids, the distribution network is undergoing a digital transformation, making the power distribution system increasingly complex. Edge computing shifts computation from the central control station to the distribution substations, thus enabling true distributed autonomy in power system operations. Taking an edge-computing-based digital substation as an example, this paper proposes a deep neural networks-based voltage regulation strategy for PV-rich distribution networks. However, executing tasks on resource-constrained edge devices faces several challenges, including data flow congestion, the inapplicability of conventional modelling and algorithm, and low computational efficiency. Therefore, we employ a unified weights neural network for Volt-Var control to achieve compression of the network parameters while still achieving differentiated action output. Furthermore, a carefully designed pipeline parallel computing structure is employed to simultaneously perform computations at different levels, further improving computational efficiency. The tested results show that, compared with existing methods, the proposed approach effectively mitigates voltage violations, improves storage efficiency and computational speed, and maintains robust performance under communication failures with partial observation, highlighting its resilience and potential for edge deployment.

Drone flight safety and operational efficiency are challenged in the landing phase, especially in windy conditions. While flight control should be a key focus, flight trajectory also plays a critical role in landing. Inspired by the multiple objectives, wind sensory capability, and skill learning of avian species, this study proposes a reinforcement learning-based trajectory planner for a drone to perform trajectory planning in the wind, aiming to balance multiple landing-related objectives based on onboard wind sensory capability and address the safety-operational efficiency dilemma of a landing site. Through four key experiments, this study demonstrates successful training, balanced landing performance, and strong generalization capability of the trajectory planner. The experiments highlight the importance of velocity sensory capability while indicating that wind sensory capability is less critical to the trajectory planner. The proposed framework with multiple objectives, wind sensory capability, and skill learning can benefit applications such as improving drone performance.

Layered composite structures inspired by biological tissues can exhibit out-of-plane negative Poisson's ratio, but identifying layups that maximize auxetic performance is challenging in high-dimensional designs. Here, we introduce an inverse design framework that searches for laminate layups with minimum Poisson's ratio. The approach combines multi-start resampling with machine learning-guided clustering to map layup families across layer numbers. Analytical relations from laminate mechanics link ply angles to effective properties, and computer simulations with laboratory measurements validate the predicted minima. The analysis resolves three layup categories, explains how shear-strain mismatch across bonded plies drives through-thickness auxetic expansion, and shows that simple symmetry rules reduce the search space. The framework reproduces previously reported minima and uncovers layups that approach lower Poisson's ratios under practical constraints. These results provide a physics-grounded, data-efficient route to engineer layered composite structures with strong auxetic responses and offer concise design rules for impact mitigation, vibration control, and flexible structures.

Gold-coated tilted fiber Bragg gratings have established themselves as powerful plasmonic biosensors, but their widespread deployment remains hindered by the need for costly, high-resolution interrogators and complex signal processing. Here, we demonstrate that tilted fiber Bragg gratings sensors can be effectively interrogated using a low-cost, coarsely resolved fiber Bragg grating interrogator with only 256 pixels spanning 45 nm, corresponding to a low resolution (~180 pm, 10 times coarser than standard interrogators). By applying a fast Fourier transform-based demodulation technique to the dense, comb-like cladding mode spectrum, we extract robust sensing information using only a narrow spectral window of a few tens of nanometers. This dramatically reduces hardware and computational requirements while preserving high sensitivity. We validate our approach in both refractometry and biosensing, targeting the clinically relevant biomarker Proteinase 3. Furthermore, we show that temperature cross-sensitivity can be compensated directly within this narrow spectral range by tracking a dedicated cladding mode resonance, eliminating the need to reference the Bragg mode. These advances pave the way for compact, cost-effective, and user-friendly plasmonic fiber sensor systems deployable in real-world biomedical environments.

Wireless communication at higher frequency bands has attracted research interest for fifth generation and beyond (5GB) wireless networks due to the large amount of unused bandwidth at these frequencies. However, there are substantial challenges associated with higher frequency bands due to the high path loss of the propagation environment and the high power consumption of the transceivers. Hybrid beamforming with massive multiple-input multiple-output (MIMO) has emerged as a solution to these problems by combining the performance and flexibility of digital beamforming with the energy efficiency of analog beamforming. Optical beamforming has recently been considered as an alternative to implement the analog component of a hybrid beamformer, which may offer improvements in size, weight and power consumption in comparison to conventional electronics. This paper proposes a new approach to implement an optical beamforming system based on photonic vector modulators using tunable photonic filters. Our experimental demonstration of the proposed optical beamformer shows that microring resonator (MRR)-based photonic vector modulators can be calibrated to achieve a root-mean-square (RMS) phase error of better than 2° and an amplitude error of 0.3 dB. Our findings identify a pathway to realize large-scale, fully-connected hybrid beamformers by leveraging compact and low loss photonic resonators.